Relevant bibliographies by topics / Positive Matrix Factorization (PMF)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Positive Matrix Factorization (PMF)'

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Published: 4 June 2021
Last updated: 22 June 2024

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Journal articles on the topic "Positive Matrix Factorization (PMF)"

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Rusanen, Anton, Anton Björklund, Manousos I. Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach. "A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization." Atmospheric Measurement Techniques 17, no. 4 (February 22, 2024): 1251–77. http://dx.doi.org/10.5194/amt-17-1251-2024.

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Abstract. The concentrations of atmospheric particulate matter and many of its constituents are temporally auto-correlated. However, this information has not been utilized in source apportionment methods. Here, we present a Bayesian matrix factorization model (BAMF) that considers the temporal auto-correlation of the components (sources) and provides a direct error estimation. The performance of BAMF is compared with positive matrix factorization (PMF) using synthetic Time-of-Flight Aerosol Chemical Speciation Monitor data, representing different urban environments from typical European towns to megacities. We find that BAMF resolves sources with overall higher factorization performance (temporal behavior and bias) than PMF on all datasets with temporally auto-correlated components. Highly correlated components continue to be challenging and ancillary information is still required to reach good factorizations. However, we demonstrate that adding even partial prior information about the chemical composition of the components to BAMF improves the factorization. Overall, BAMF-type models are promising tools for source apportionment and merit further research.
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Ryoo, Ilhan, Jieun Park, Taeyeon Kim, Jiwon Ryu, Yeonseung Cheong, Joonyoung Ahn, and Seung-Muk Yi. "Study of PM2.5 Using PMF Receptor Model and Advancement of Source Apportionment." Journal of Korean Society for Atmospheric Environment 38, no. 4 (August 31, 2022): 493–507. http://dx.doi.org/10.5572/kosae.2022.38.4.493.

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Molnár, Peter, Sandra Johannesson, and Ulrich Quass. "Source Apportionment of PM2.5 Using Positive Matrix Factorization (PMF) and PMF with Factor Selection." Aerosol and Air Quality Research 14, no. 3 (2014): 725–33. http://dx.doi.org/10.4209/aaqr.2013.11.0335.

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Behl, Rachna, and Indu Kashyap. "Locus recommendation using probabilistic matrix factorization techniques." Ingeniería Solidaria 17, no. 1 (January 11, 2021): 1–25. http://dx.doi.org/10.16925/2357-6014.2021.01.10.

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Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20. Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to the users. Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF. This is because the technique is based on gamma distribution to the model user and item matrix. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well. Conclusion: The motive of the work is to identify the best technique for recommending locations with the highest accuracy and allow users to choose from a plethora of available locations; the best and interesting location based on the individual’s profile. Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models. Limitations: User’s contextual information like demographics, social and geographical preferences have not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.
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Drosatou, Anthoula D., Ksakousti Skyllakou, Georgia N. Theodoritsi, and Spyros N. Pandis. "Positive matrix factorization of organic aerosol: insights from a chemical transport model." Atmospheric Chemistry and Physics 19, no. 2 (January 24, 2019): 973–86. http://dx.doi.org/10.5194/acp-19-973-2019.

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Abstract. Factor analysis of aerosol mass spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources accurately. We performed PMF and multilinear engine (ME-2) analysis on the predictions of a state-of-the-art chemical transport model (PMCAMx-SR, Particulate Matter Comprehensive Air Quality Model with extensions – source resolved) during a photochemically active period for specific sites in Europe in an effort to interpret the diverse factors usually identified by PMF analysis of field measurements. Our analysis used the predicted concentrations of 27 OA components, assuming that each of them is “chemically different” from the others. The PMF results based on the chemical transport model predictions are quite consistent (same number of factors and source types) with those of the analysis of AMS measurements. The estimated uncertainty of the contribution of fresh biomass burning is less than 30 % and of the other primary sources less than 40 %, when these sources contribute more than 20 % to the total OA. The PMF uncertainty increases for smaller source contributions, reaching a factor of 2 or even 3 for sources which contribute less than 10 % to the OA. One of the major questions in PMF analysis of AMS measurements concerns the sources of the two or more oxygenated OA (OOA) factors often reported in field studies. Our analysis suggests that these factors include secondary OA compounds from a variety of anthropogenic and biogenic sources and do not correspond to specific sources. Their characterization in the literature as low- and high-volatility factors is probably misleading, because they have overlapping volatility distributions. However, the average volatility of the one often characterized as a low-volatility factor is indeed lower than that of the other (high-volatility factor). Based on the analysis of the PMCAMx-SR predictions, the first oxygenated OA factor includes mainly highly aged OA transported from outside Europe, but also highly aged secondary OA from precursors emitted in Europe. The second oxygenated OA factor contains fresher secondary organic aerosol from volatile, semivolatile, and intermediate volatility anthropogenic and biogenic organic compounds. The exact contribution of these OA components to each OA factor depends on the site and the prevailing meteorology during the analysis period.
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Ulbrich, I. M., M. R. Canagaratna, Q. Zhang, D. R. Worsnop, and J. L. Jimenez. "Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data." Atmospheric Chemistry and Physics 9, no. 9 (May 5, 2009): 2891–918. http://dx.doi.org/10.5194/acp-9-2891-2009.

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Abstract. The organic aerosol (OA) dataset from an Aerodyne Aerosol Mass Spectrometer (Q-AMS) collected at the Pittsburgh Air Quality Study (PAQS) in September 2002 was analyzed with Positive Matrix Factorization (PMF). Three components – hydrocarbon-like organic aerosol OA (HOA), a highly-oxygenated OA (OOA-1) that correlates well with sulfate, and a less-oxygenated, semi-volatile OA (OOA-2) that correlates well with nitrate and chloride – are identified and interpreted as primary combustion emissions, aged SOA, and semivolatile, less aged SOA, respectively. The complexity of interpreting the PMF solutions of unit mass resolution (UMR) AMS data is illustrated by a detailed analysis of the solutions as a function of number of components and rotational forcing. A public web-based database of AMS spectra has been created to aid this type of analysis. Realistic synthetic data is also used to characterize the behavior of PMF for choosing the best number of factors, and evaluating the rotations of non-unique solutions. The ambient and synthetic data indicate that the variation of the PMF quality of fit parameter (Q, a normalized chi-squared metric) vs. number of factors in the solution is useful to identify the minimum number of factors, but more detailed analysis and interpretation are needed to choose the best number of factors. The maximum value of the rotational matrix is not useful for determining the best number of factors. In synthetic datasets, factors are "split" into two or more components when solving for more factors than were used in the input. Elements of the "splitting" behavior are observed in solutions of real datasets with several factors. Significant structure remains in the residual of the real dataset after physically-meaningful factors have been assigned and an unrealistic number of factors would be required to explain the remaining variance. This residual structure appears to be due to variability in the spectra of the components (especially OOA-2 in this case), which is likely to be a key limit of the retrievability of components from AMS datasets using PMF and similar methods that need to assume constant component mass spectra. Methods for characterizing and dealing with this variability are needed. Interpretation of PMF factors must be done carefully. Synthetic data indicate that PMF internal diagnostics and similarity to available source component spectra together are not sufficient for identifying factors. It is critical to use correlations between factor and external measurement time series and other criteria to support factor interpretations. True components with <5% of the mass are unlikely to be retrieved accurately. Results from this study may be useful for interpreting the PMF analysis of data from other aerosol mass spectrometers. Researchers are urged to analyze future datasets carefully, including synthetic analyses, and to evaluate whether the conclusions made here apply to their datasets.
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Wu, Jiaying, Zhijun Wu, and Robert Holländer. "The application of Positive Matrix Factorization (PMF) to eco-efficiency analysis." Journal of Environmental Management 98 (May 2012): 11–14. http://dx.doi.org/10.1016/j.jenvman.2011.12.022.

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Han, J. S., K. J. Moon, S. J. Lee, Y. J. Kim, S. Y. Ryu, S. S. Cliff, and S. M. Yi. "Size-resolved source apportionment of ambient particles by positive matrix factorization." Atmospheric Chemistry and Physics Discussions 5, no. 4 (July 22, 2005): 5223–52. http://dx.doi.org/10.5194/acpd-5-5223-2005.

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Abstract. Size- and time-resolved aerosol samples were collected using an eight-stage DRUM sampler from 29 March to 29 May in 2002 at Gosan, Jeju Island, Korea, which is one of the representative background sites in East Asia. These samples were analyzed using synchrotron X-ray fluorescence for 3-h average concentrations of 19 elements consisting of S, Si, Al, Fe, Ca, Cl, Cu, Zn, Ti, K, Mn, Pb, Ni, V, Se, As, Rb, Cr, Br. The size-resolved data sets were then analyzed using the positive matrix factorization (PMF) technique in order to identify possible sources and estimate their contribution to particulate matter mass. PMF analysis uses the uncertainty of the measured data to provide an optimal weighting. Fifteen sources were resolved in eight size ranges (0.07–12 µm) and included Chinese aerosol, soil dust, sea salt, biomass burning, coal combustion, oil heating furnace, residual oil-fired boiler, municipal incineration, nonferrous metal source, ferrous metal source, gasoline vehicle, diesel vehicle, copper smelter, and volcano emission. PMF analysis of size-resolved source contributions showed that natural sources represented by soil dust, sea salt and Chinese aerosol contributed about 79% to the predicted primary PM mass in the coarse size range (1.15–12 µm). On the other hand, anthropogenic sources such as coal combustion and biomass burning contributed about 60% in the fine size range (0.56–2.5 µm). The diesel vehicle source contributed the most in the ultra-fine size range (0.07–0.56 µm) and was responsible for about 52% of the primary PM mass.
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Ulbrich, I. M., M. R. Canagaratna, Q. Zhang, D. R. Worsnop, and J. L. Jimenez. "Interpretation of organic components from positive matrix factorization of aerosol mass spectrometric data." Atmospheric Chemistry and Physics Discussions 8, no. 2 (April 9, 2008): 6729–91. http://dx.doi.org/10.5194/acpd-8-6729-2008.

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Abstract. The organic aerosol (OA) dataset from an Aerodyne Aerosol Mass Spectrometer (Q-AMS) collected at the Pittsburgh Air Quality Study in September 2002 was analyzed for components with Positive Matrix Factorization (PMF). Three components – hydrocarbon-like organic aerosol OA (HOA), a highly-oxygenated OA (OOA-I) that correlates well with sulfate, and a less-oxygenated, semi-volatile OA (OOA-II) that correlates well with nitrate and chloride – are identified and interpreted as primary combustion emissions, aged SOA, and semivolatile, less aged SOA, respectively. The complexity of interpreting the PMF solutions of unit mass resolution (UMR) AMS data is illustrated by a detailed analysis of the solutions as a function of number of components and rotational state. A public database of AMS spectra has been created to aid this type of analysis. A sensitivity analysis with realistic synthetic data is also used to characterize the behavior of PMF for choosing the best number of factors, rotations of non-unique solutions, and the retrievability of more (or less) correlated factors. The ambient and synthetic data indicate that the variation of the PMF quality of fit parameter (Q, a normalized chi-squared metric) vs. number of factors in the solution is useful to identify the minimum number of factors, but more detailed analysis and interpretation is needed to choose the best number of factors. The maximum value of the rotational matrix is not useful for determining the best number of factors. In synthetic datasets, factors are "split" into two or more components when solving for more factors than were used in the input. Elements of the "splitting" behavior are observed in solutions of real datasets with several factors. Significant structure remains in the residual of the real dataset after physically-meaningful factors have been assigned and an unrealistic number of factors would be required to explain the remaining variance. This residual structure appears to be due to variability in the spectra of the components (especially OOA-II in this case), which is likely to be a key limit of the retrievability of components from AMS datasets using PMF and similar methods that need to assume constant component mass spectra. Methods for characterizing and dealing with this variability are needed. Values of the rotational parameter (FPEAK) near zero appear to be most appropriate for these datasets. Interpretation of PMF factors must be done carefully. Synthetic data indicate that PMF internal diagnostics and similarity to available source component spectra together are not sufficient for identifying factors. It is critical to use correlations between factor time series and external measurement time series to support factor interpretations. Components with <5% of the mass or with high correlation (R>0.9) with other components are suspect and should be interpreted with care. Results from this study may be useful for interpreting the PMF analysis of data from other aerosol mass spectrometers.
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Han, Sangwoo, Chunsang Lee, KyungChan Kim, Subin Lee, and Jinseok Han. "PMF(Positive Matrix Factorization) 모델을 활용한 대전지역의 초미세먼지 배출원별 기여도 추정 연구." Journal of the Korean Society of Urban Environment 21, no. 4 (December 31, 2021): 289–98. http://dx.doi.org/10.33768/ksue.2021.21.4.289.

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Dissertations / Theses on the topic "Positive Matrix Factorization (PMF)"

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Peña, Sanchez Carlos Alberto. "Quantification of Anthropogenic and Natural Sources of Fine Particles in Houston, Texas Using Positive Matrix Factorization." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149652/.

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Texas, due to its geographical area, population, and economy is home to a variety of industrialized areas that have significant air quality problems. These urban areas are affected by elevated levels of fine particulate matter (PM2.5). The primary objective of this study was to identify and quantify local and regional sources of air pollution affecting the city of Houston, Texas. Positive Matrix Factorization (PMF) techniques were applied to observational datasets from two urban air quality monitoring sites in Houston from 2003 through 2008 in order to apportion sources of pollutants affecting the study region. Data from 68 species for Aldine and 91 for Deer Park were collected, evaluated, and revised to create concentration and uncertainty input files for the PMF2 and EPA PMF (PMF3) source apportionment models. A 11-sources solution for Aldine and 10-sources for Deer Park were identified as the optimal solutions with both models. The dominant contributors of fine particulate matter in these sites were found to be biomass burnings (2%-8.9%), secondary sulfates I (21.3%-7.6%) and II (38.8%-22.2%), crustal dust (8.9%-10.9%), industrial activities (10.9%-4.2%), traffic (23.1%-15.6%), secondary nitrates (4.4%-5.5%), fresh (1%-1.6%) and aged(5.1%-4.6%) sea salt and refineries (1.3%-0.6%), representing a strong case to confirm the high influence of local activities from the industrial area and the ship channel around the Houston channel. Additionally, potential source contribution function (PSCF) and conditional probability function (CPF) analyses were performed to identify local and regional source-rich areas affecting this urban airshed during the study period. Similarly, seasonal variations and patterns of the apportioned sources were also studied in detail.
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Ciani, Renato. "Um estudo de sensibilidade da fatoração PMF - Positive Matrix Factorization - em relação às medidas de incerteza das variáveis." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-07092017-165948/.

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A fatoração PMF - Positive Matrix Factorization - é um método de resolução de problemas em que variáveis observadas conjuntamente são modeladas como a combinação linear de fatores potenciais, representada pela multiplicação de duas matrizes. Este método tem sido utilizado principalmente em áreas de estudo em que as variáveis observadas são sempre não negativas, e quando é aplicada uma modelagem fatorial ao problema. Assume-se a premissa de que são resultantes da multiplicação de duas matrizes com entradas não negativas, ou seja, os fatores potenciais, e os poderadores da combinação linear são desconhecidos, e têm valores não negativos. Neste método além da possibilidade de restringir a busca dos valores das matrizes da fatoração a valores não negativos, também é possível incluir a medida de incerteza relacionada a cada observação no processo de obtenção da fatoração como um modo de reduzir o efeito indesejado que valores outliers podem causar no resultado. Neste trabalho é feito um estudo de sensibilidade da fatoração PMF em relação a algumas medidas de incertezas presentes na literatura, utilizando o software EPA PMF 5.0 com ME-2. Para realizar este estudo foi desenvolvida uma metodologia de simulação de base de dados a partir de perfis de fontes emissoras conhecidas incluindo valores outliers, e aplicação sequencial da fatoração PMF com o software ME-2 nas bases de dados simuladas.
The PMF factorization - Positive Matrix Factorization - is a problem solving method in which jointly observed variables are modeled as a linear combination of potential factors, represented by the multiplication of two matrices. This method has been used primarily in study areas in which the observed variables are always non negative, and when it is applied a factor modeling in the problem. It is made the assumption that the variables in study come from the two matrices multiplication both having non negative components, i.e., the potential factors, and the linear combination values are unknown, and all of them have non negative values. In this method, besides the possibility of constraining the search of the matrix factorization values on non negative values, it is also possible to include the uncertainty measure related to each observation on factorization process as a way to reduce the undesired effect which outliers can cause to the outcome. This paper presents a study of the sensitivity of the factorization PMF over some uncertainties measures present in literature, using EMP PMF 5.0 with ME-2 software. To carry out this study was developed a methodology of database simulation from known emitting sources profiles including outliers values, and a sequential application of PMF factorization with ME-2 software in simulated databases.
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Scerri, Mark. "The use of Positive Matrix Factorization (PMF) in source apportionment of ambient aerosol in the Central Mediterranean." Phd thesis, Digilabs srls, 2019. https://tuprints.ulb.tu-darmstadt.de/9172/13/Mark%20Scerri%20Cumulative%20thesis%20copy%20Signed.pdf.

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Positive Matrix Factorization (PMF) is a widely used receptor modelling technique in order to determine the nature and contribution of the different aerosol sources modulating ambient levels of particulate matter at a receptor. This cumulative thesis together with the papers included within, reports the results of three source apportionment exercises: a) the isolation of the natural contribution to PM10 levels at a rural background site in Malta; b) the estimation of the contributions of the sources driving PM2.5 levels at a traffic hotspot in Malta and c) a methodological investigation of how PMF can be used on a smaller dataset using samples taken from an agricultural area in Apulia, South eastern Italy. The study on the magnitude of the natural contribution to PM10 involved a sampling campaign at a rural background station in Għarb, Gozo (one of the islands in the Maltese archipelago). This sampling campaign resulted in the collection 224 samples, which were subsequently characterised by inductively coupled plasma mass spectrometry (ICP – MS) and ion chromatography (IC) respectively for, their metallic and ionic content. The use of data resulting from this analysis with PMF resulted in the isolation of the two natural sources expected to be of relevance to Malta (marine aerosol and Saharan dust) as well as in the estimation of their apportionment. PMF also resolved three additional sources: a local crustal source, and two secondary inorganic aerosol components (one rich in nitrate and the other rich in sulfate). The natural sources jointly accounted for approximately 39% of the measured PM10, which is at the upper end of the 0.5 – 58% range determined by previous studies for natural contributions in Europe. A total of 180 membranes sampled throughout 2016 were used in the study on the sources of PM2.5 at a traffic site. These membranes were analysed for: elemental concentrations (using X-ray fluorescence spectroscopy, XRF); ionic content (using IC) and for black carbon – BC (using an optical method). The use of this chemical database with PMF resulted in the isolation of 7 aerosol sources, 4 of which were common to the exercise carried out on PM10 at the rural background site (all the sources except for the local crustal source). The additional three sources isolated at this site were traffic, shipping and fireworks. The isolation of the latter component is itself an interesting result, because it shows that a seasonal activity such as the letting of fireworks during the summer village feasts affects the annual levels of PM2.5. Additionally, this component probably has an effect on human health due to its chemical composition. This work will also provide evidence-based information to the policy makers on the emission reductions required in order for the PM2.5 levels to be compliant with the annual air quality guideline issued by the World Health Organization. Finally, a fundamental methodological investigation on how PMF can be used on a small dataset was carried out. This study is based on 29 PM10 and 33 PM2.5 samples collected from a rural area in Apulia, Italy. PMF did not work correctly when the datasets for the two different fractions were used separately. The datasets were therefore aggregated into a single chemical database of 62 samples and this was then used with PMF. A 5-factor model, which exhibited a fairly good rotational stability was the result of this modelling exercise. This was subsequently further improved through the imposition of constraints based on the chemical constitution of the aerosol sources affecting this receptor, which is made possible by the new features included in the United States Environment Protection Agency PMF version 5. Given the size of the dataset the, the uncertainties in the solution returned by PMF were fully characterised using all the error estimation methodologies included in this version of PMF. Additionally, the results of the PMF modelling were validated against those returned by two other models, Constrained Weighted Non-negative Matrix Factorization (CW – NMF) and Chemical Mass Balance (CMB) as well as through the use of other statistical parameters. These results essentially confirm the validity of the model returned by PMF and indicate that the latter model extracted all the information about the aerosol sources affecting the receptor from the speciation data.
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Lingwall, Jeff W. "Bayesian and Positive Matrix Factorization approaches to pollution source apportionment /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1295.pdf.

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Scerri, Mark [Verfasser], Stephan [Akademischer Betreuer] Weinbruch, and Konrad [Akademischer Betreuer] Kandler. "The use of Positive Matrix Factorization (PMF) in source apportionment of ambient aerosol in the Central Mediterranean / Mark Scerri ; Stephan Weinbruch, Konrad Kandler." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2019. http://d-nb.info/1199006483/34.

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Oroumiyeh, Farzan. "Temporal Interpolation Modeling of Cincinnati’s Central Air Quality Monitoring Data for Use in Epidemiologic Studies: PM2.5 Source Apportionment using Positive Matrix Factorization (PMF)." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504800976355814.

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Srivastava, Deepchandra. "Improving the discrimination of primary and secondary sources of organic aerosol : use of molecular markers and different approaches." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0055/document.

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Les aérosols organiques (AO), issus de nombreuses sources et de différents processus atmosphériques, ont un impact significatif sur la qualité de l’air et le changement climatique. L’objectif de ce travail de thèse était d’acquérir une meilleure connaissance de l’origine des AO par l’utilisation de marqueurs organiques moléculaires au sein de modèles source-récepteur de type positive matrix factorization (PMF). Ce travail expérimental était basé sur deux campagnes de prélèvements réalisées à Grenoble (site urbain) au cours de l’année 2013 et dans la région parisienne (site péri-urbain du SIRTA, 25 km au sud-ouest de Paris) lors d’un intense épisode de pollution aux particules (PM) en Mars 2015. Une caractérisation chimique étendue (de 139 à 216 espèces quantifiées) a été réalisée et l’utilisation de marqueurs moléculaires primaires et secondaires clés dans la PMF a permis de déconvoluer de 9 à 11 sources différentes de PM10 (Grenoble et SIRTA, de façon respective) incluant aussi bien des sources classiques (combustion de biomasse, trafic, poussières, sels de mer, nitrate et espèces inorganiques secondaires) que des sources non communément résolues telles que AO biogéniques primaires (spores fongiques et débris de plantes), AO secondaires (AOS) biogéniques (marin, oxydation de l’isoprène) et AOS anthropiques (oxydation des hydrocarbures aromatiques polycycliques (HAP) et/ou des composés phénoliques). En outre, le jeu de données obtenu pour la région parisienne à partir de prélèvements sur des pas de temps courts (4h) a permis d’obtenir une meilleure compréhension des profils diurnes et des processus chimiques impliquées. Ces résultats ont été comparés à ceux issus d’autres techniques de mesures (en temps réel, ACSM (aerosol chemical speciation monitor) et analyse AMS (aerosol mass spectrometer) en différée) et/ou d’autres méthodes de traitement de données (méthodes traceur EC (elemental carbon) et traceur AOS). Un bon accord a été obtenu entre toutes les méthodes en termes de séparation des fractions primaires et secondaires. Cependant, et quelle que soit l’approche utilisée, la moitié de la masse d’AOS n’était toujours pas complètement décrite. Ainsi, une nouvelle approche d’étude des sources de l’AO a été développée en combinant les mesures en temps réel (ACSM) et celles sur filtres (marqueurs moléculaires organiques) et en utilisant un script de synchronisation des données. L’analyse PMF combinée a été réalisée sur la matrice de données unifiée. 10 facteurs AO, incluant 4 profils chimiques différents en lien avec la combustion de biomasse, ont été mis en évidence. Par rapport aux approches conventionnelles, cette nouvelle méthodologie a permis d’obtenir une meilleure compréhension des processus atmosphériques liés aux différentes sources d’AO
Organic aerosols (OAs), originating from a wide variety of sources and atmospheric processes, have strong impacts on air quality and climate change. The present PhD thesis aimed to get a better understanding of OA origins using specific organic molecular markers together with their input into source-receptor model such as positive matrix factorization (PMF). This experimental work was based on two field campaigns, conducted in Grenoble (urban site) over the 2013 year and in the Paris region (suburban site of SIRTA, 25 km southwest of Paris) during an intense PM pollution event in March 2015. Following an extended chemical characterization (from 139 to 216 species quantified), the use of key primary and secondary organic molecular markers within the standard filter-based PMF model allowed to deconvolve 9 and 11 PM10 sources (Grenoble and SIRTA, respectively). These included common ones (biomass burning, traffic, dust, sea salt, secondary inorganics and nitrate), as well as uncommon resolved sources such as primary biogenic OA (fungal spores and plant debris), biogenic secondary AO (SOA) (marine, isoprene oxidation) and anthropogenic SOA (polycyclic aromatic hydrocarbons (PAHs) and/or phenolic compounds oxidation). In addition, high time-resolution filter dataset (4h-timebase) available for the Paris region also illustrated a better understanding of the diurnal profiles and the involved chemical processes. These results could be compared to outputs from other measurement techniques (online ACSM (aerosol chemical speciation monitor), offline AMS (aerosol mass spectrometer) analyses), and/or to other data treatment methodologies (EC (elemental carbon) tracer method and SOA tracer method). A good agreement was obtained between all the methods in terms of separation between primary and secondary OA fractions. Nevertheless, and whatever the method used, still about half of the SOA mass was not fully described. Therefore, a novel OA source apportionment approach has finally been developed by combining online (ACSM) and offline (organic molecular markers) measurements and using a time synchronization script. This combined PMF analysis was performed on the unified matrix. It revealed 10 OA factors, including 4 different biomass burning-related chemical profiles. Compared to conventional approaches, this new methodology provided a more comprehensive description of the atmospheric processes related to the different OA sources
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Dufresne, Marvin. "Sources et déterminants des composés organiques volatils à Marseille." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Lille Douai, 2022. http://www.theses.fr/2022MTLD0007.

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Les Composés organiques volatils (COV) dont les hydrocarbures non méthaniques (HCNM) ont un rôle clé dans la chimie atmosphérique en tant que précurseurs de polluants secondaires tels que l’ozone (O3) et les aérosols organiques secondaires (AOS). Pourtant, les connaissances sur les émissions des HCNM restent insuffisantes provoquant de fortes incertitudes associées aux inventaires d’émissions et par conséquent sur les modèles de chimie-transport (CTM), essentiels pour la mise en place de politique de qualité de l’air efficace. Ce manque d’information est d’autant plus critique sur le bassin Méditerranéen, région particulièrement marquée par la pollution atmosphérique et le changement climatique. Dans le but d’apporter des connaissances nouvelles sur les sources et déterminants des COV sur cette région, une campagne d’observation de 18 mois a été menée de mars 2019 à août 2020 à Marseille. Elle a permis l’obtention d’une base de données unique de 70 composés hydrocarbonés pour l’étude de l’évolution de la composition en HCNM de l’atmosphère de Marseille. L’analyse des observations à l’aide du modèle source-récepteur PMF (Positive Matrix Factorization) a permis de déterminer huit sources majeures pour les composés mesurés. Le trafic routier est le premier émetteur de ces composés à Marseille pour toutes les saisons contribuant à 40 % des concentrations alors que le chauffage domestique contribue à 20 % en hiver. Une baisse marquée des émissions en HCNM dues au trafic routier a été constatée au printemps 2020 associée au confinement pour la crise sanitaire du COVID-19. Une source industrielle a été identifiée comme fortement émettrice de xylènes, espèces à fort impact potentiel sur la formation d’AOS. Enfin, les inventaires d’émissions à différentes échelles ont été comparés entre eux et avec l’évaluation issue des observations pour la zone d’étude. Il apparaît une forte variabilité sur les émissions en COV totaux mais une très bonne concordance pour les émissions en COV du trafic routier. Cette comparaison a montré que la spéciation chimique des sources d’émissions en COV est significativement plus élevée pour les inventaires dans le cas des HCNM issues de combustion (alcènes et aromatiques) ce qui est possiblement dû à une surestimation du chauffage résidentiel suivant les saisons. En outre, l’étude a montré une différence de composition chimique pour le trafic routier entre l’inventaire d’émission local et les observations
Volatil Organic Compounds (VOC) are key species because of their role as precursors of secondary pollutants such as ozone (O3) and secondary organic aerosols (SOA). However, the knowledge on VOC emissions remains insufficient, leading to high uncertainties on emission inventories and consequently on chemistry-transport models (CTM) which are crucial for the successful implementation of efficient air quality policies. This lack of information is all the more critical in the Mediterranean basin since this region is particularly affected by air pollution and climate change. In order to provide new knowledge on the sources and factors controlling VOC in this region, an 18-months field campaign took place from March 2019 to August 2020 in Marseille. It allowed to obtain a unique database of 70 non-methane hydrocarbon (NMHC) compounds for the study of the evolution of the VOC composition of the atmosphere of Marseille. The analysis of observations using the source-receptor model PMF (Positive Matrix Factorization), allowed to determine eight major NMHC emission sources for the measured compounds. Road traffic is the main emitter of these compounds in Marseille in all the seasons contributing to 40% of concentrations whereas residential heating contributes to 20% in winter. A sharp decrease of the NMHC emissions due to road traffic has been observed in Spring 2020 associated to the lockdown due to the sanitary crisis of Covid-19. An industrial source has been identified as high emitter of xylenes, species with a high potential on SOA formation. Global, regional and local emission inventories were compared to each other with the observations in the Marseille area. A high variability on the total VOC emissions but a very good agreement on the VOC emissions from road traffic. This comparison showed the chemical speciation of VOC emission sources is significantly higher for the inventories in the case of HCNM emitted by combustion (alkenes and aromatics) possibly due to an overestimation of residential heating. In addition, the study showed a difference in chemical composition for road traffic between the local emission inventory and observations
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Chevrier, Florie. "Chauffage au bois et qualité de l’air en Vallée de l’Arve : définition d’un système de surveillance et impact d’une politique de rénovation du parc des appareils anciens." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAU020/document.

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La combustion de la biomasse est l’une des sources majoritaires de particules atmosphériques en périodes hivernales dans les vallées alpines, et particulièrement en vallée de l’Arve où des dépassements des seuils européens sont très régulièrement observés. Ceci a conduit à la mise en place d’un large programme de remplacement des dispositifs de chauffage au bois les moins performants dans le cadre d’une des actions du Plan de Protection de l’Atmosphère, le Fond Air Bois. Le projet DECOMBIO (DÉconvolution de la contribution de la COMbustion de la BIOmasse aux PM10 dans la vallée de l’Arve) a ainsi été mis en place en octobre 2013 afin de mesurer l’impact de cette politique de rénovation des appareils de chauffage au bois sur la qualité de l’air. C’est dans ce programme que s’inscrivent ces travaux de thèse dont l’objectif principal est de valider les méthodologies mises en place en routine pour permettre une déconvolution rapide de la combustion de la biomasse et mettre en relation les éventuels changements observés avec les avancées des remplacements de dispositifs de chauffage au bois domestiques.Pour mener à bien ce travail, trois sites, représentant les différentes situations de la vallée de l’Arve, ont été instrumentés (Marnaz, Passy et Chamonix) afin de suivre en continu, et tout au long du projet DECOMBIO, l’évolution des concentrations atmosphériques du Black Carbon (BC) et des traceurs moléculaires permettant de distinguer la contribution de la combustion de la biomasse des autres types de combustion. Un important jeu de données a été acquis entre novembre 2013 et octobre 2014 grâce à des prélèvements réguliers sur filtre permettant une caractérisation très fine de la composition chimique des particules atmosphériques. L’utilisation de l’approche statistique « Positive Matrix Factorization » (PMF) a permis de mieux appréhender les différentes sources entrant en jeu dans les émissions de particules au sein de cette vallée avec notamment un intérêt particulier pour les émissions de la combustion de la biomasse. Le développement de cette méthodologie d’attribution et de quantification des sources de particules basé sur l’utilisation de traceurs organiques spécifiques, de contraintes particulières appliquées à ce modèle et de données de déconvolution de la matière carbonée constitue une avancée importante dans la définition des facteurs sources issus de ce modèle.Les méthodologies développées au cours de ce travail, permettant une amélioration des connaissances et des contributions des sources, constituent donc des outils directement utilisables par les Associations Agréées de Surveillance de la Qualité de l’Air (AASQA), notamment pour l’évaluation quantitative des mesures prises pour améliorer la qualité de l’air dans le cadre de Plans de Protection de l’Atmosphère, entre autres celui de la vallée de l’Arve
Biomass burning is one of the major sources of atmospheric particles during wintertime in Alpine valleys, and more especially in the Arve valley where exceedances of the European regulated limit value are regularly observed. This situation led to the establishment of an important program of replacement of old wood stoves with new ones as part of an action of an Atmospheric Protection Plan (APP), the “Fonds Air Bois”. The research program DECOMBIO (“DÉconvolution de la contribution de la COMbustion de la BIOmasse aux PM10 dans la vallée de l’Arve”) has been set up in October 2013 to estimate the impact of this wood stoves renewal policy on air quality. This thesis works be incorporated within this program and have for main objective to validate methodologies used in routine to enable a fast deconvolution of the biomass burning source and to compare any observed changes with progress of wood stove changeout.To complete this work, three sites, representing the different situations of the Arve valley, were instrumented (Marnaz, Passy and Chamonix) to monitor the continuing evolution of atmospheric concentrations of Black Carbon (BC) and molecular markers enabling to distinguish between the biomass burning contribution and that of other types of combustion. A large dataset was acquired between November 2013 and October 2014 thanks to regular filter samples enabling a vast chemical characterization of PM10. The use of statistical analysis “Positive Matrix Factorization” (PMF) has led to an enhanced appreciation of particle emission sources within this valley with a focus on biomass burning emissions. The development of this methodology of identification and source apportionment based on the use of specific organic markers, specific constraints and data from carbonaceous matter deconvolution is an important progress in definition of factors from this model.The developed methodologies during this work, enabling an improvement of knowledges and source apportionment, are tools directly usable by French Accredited Associations for Air Quality Monitoring, especially for the quantitative assessment of actions introduced to improve air quality as part of Atmospheric Protection Plans, for example the one in the Arve valley
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Shaltanis, Jennifer Lynn Hehl. "Source apportionment of Spokane fine fraction air pollution using the Spokane health effects database and positive matrix factorization." Online access for everyone, 2006. http://www.dissertations.wsu.edu/Dissertations/Fall2006/j_shaltanis_112606.pdf.

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Books on the topic "Positive Matrix Factorization (PMF)"

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1953-, Spitkovskiĭ Ilya M., and Woerdeman Hugo J. 1962-, eds. Abstract band method via factorization positive and band extensions of multivariable almost periodic matrix functions, and spectral estimation. Providence, RI: American Mathematical Society, 2002.

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Lee, Patrick Kin Hung. Receptor modeling on Canadian atmospheric fine particulate matter (PM2.5) by positive matrix factorization. 2002, 2002.

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Book chapters on the topic "Positive Matrix Factorization (PMF)"

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Lyche, Tom. "LDL* Factorization and Positive Definite Matrices." In Numerical Linear Algebra and Matrix Factorizations, 83–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36468-7_4.

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Bart, Harm, Marinus A. Kaashoek, and André C. M. Ran. "Factorization of positive definite rational matrix functions." In A State Space Approach to Canonical Factorization with Applications, 181–96. Basel: Birkhäuser Basel, 2010. http://dx.doi.org/10.1007/978-3-7643-8753-2_10.

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Bart, Harm, Marinus A. Kaashoek, and André C. M. Ran. "Factorization of positive real rational matrix functions." In A State Space Approach to Canonical Factorization with Applications, 291–300. Basel: Birkhäuser Basel, 2010. http://dx.doi.org/10.1007/978-3-7643-8753-2_16.

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Delchamps, David F. "Positive Definiteness, Matrix Factorization, and an Imperfect Analogy." In State Space and Input-Output Linear Systems, 162–75. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-3816-4_13.

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Shi, Yilong, Hong Lin, and Yuqiang Li. "IU-PMF: Probabilistic Matrix Factorization Model Fused with Item Similarity and User Similarity." In Cloud Computing and Security, 747–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68542-7_65.

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Omizo, Ryan M. "Be positive." In Corpora and Rhetorically Informed Text Analysis, 167–89. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/scl.109.08omi.

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This chapter proposes a novel method that deploys non-negative matrix factorization to extract topic models from texts. This topic modeling process reveals how terms and DocuScope Language Action Type Analysis (LATs) align, providing robust information on what texts are about and how they are organized rhetorically. Moreover, the non-negative nature of the topics means that each derived topic can be viewed as a sum of topical features, which can greatly ease the interpretive process. To elucidate and benchmark this method, I apply it to a well-known 20 Newsgroups dataset and sample the results.
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Xiao, Ruliang, Yinuo Li, Hongtao Chen, Youcong Ni, and Xin Du. "SRSP-PMF: A Novel Probabilistic Matrix Factorization Recommendation Algorithm Using Social Reliable Similarity Propagation." In Intelligent Computing Theories and Methodologies, 80–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22186-1_8.

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Vinagre, João, Alípio Mário Jorge, and João Gama. "Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback." In User Modeling, Adaptation, and Personalization, 459–70. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_41.

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Scott, Jennifer, and Miroslav Tůma. "Sparse Cholesky Solver: The Factorization Phase." In Nečas Center Series, 73–88. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25820-6_5.

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AbstractHaving considered the symbolic phase of a sparse Cholesky solver in the previous chapter, the focus of this chapter is the subsequent numerical factorization phase. If A is a symmetric positive definite (SPD) matrix, then it is factorizable (strongly regular) and (in exact arithmetic) its Cholesky factorization A = LLT exists. LDLT factorizations of general symmetric indefinite matrices are considered in Chapter 7.
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Krupnik, Ilya, Naum Krupnik, and Vladimir Matsaev. "On Canonical Factorization of Dissipative and Positive Matrix Functions Relative to Non-Simple Contours." In Singular Integral Operators and Related Topics, 288–94. Basel: Birkhäuser Basel, 1996. http://dx.doi.org/10.1007/978-3-0348-9040-3_10.

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Conference papers on the topic "Positive Matrix Factorization (PMF)"

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Saha, Arnab, Bhaskar Sen Gupta, Sandhya Patidar, and Nadia Martínez-Villegas. "Estimating Source Apportionment of Heavy Metals Contamination in Surface Soil based on Positive Matrix Factorization (PMF) model around Cerrito Blanco in San Luis Potosi, Mexico." In The 4th International Electronic Conference on Geosciences. Basel, Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/iecg2022-13746.

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Lahat, Dana, and Cedric Fevotte. "Positive Semidefinite Matrix Factorization Based on Truncated Wirtinger Flow." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287560.

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Masalmah, Yahya M., and Miguel Vélez-Reyes. "Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization." In Defense and Security Symposium, edited by Harold H. Szu. SPIE, 2006. http://dx.doi.org/10.1117/12.667976.

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Yoshii, Kazuyoshi, Katsutoshi Itoyama, and Masataka Goto. "Student's T nonnegative matrix factorization and positive semidefinite tensor factorization for single-channel audio source separation." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471635.

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Masalmah, Yahya M., Miguel Velez-Reyes, and Samuel Rosario-Torres. "An algorithm for unsupervised unmixing of hyperspectral imagery using positive matrix factorization." In Defense and Security, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2005. http://dx.doi.org/10.1117/12.605672.

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Hara, Tomofumi, Yuki Sumiya, and Kazuhide Nakata. "Temporal Positive Collective Matrix Factorization for Interpretable Trend Analysis in Recommender Systems." In 2023 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2023. http://dx.doi.org/10.1109/icdmw60847.2023.00011.

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Rust, Tyler J., Jeffrey T. Pietras, and Joseph R. Graney. "CHARACTERIZING UTICA SHALE DEPOSITIONAL PROCESSES USING PORTABLE XRF ANALYSES AND POSITIVE MATRIX FACTORIZATION." In 53rd Annual GSA Northeastern Section Meeting - 2018. Geological Society of America, 2018. http://dx.doi.org/10.1130/abs/2018ne-311243.

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Rust, Tyler J., Daniel R. Miserendino, Jeffrey T. Pietras, and Joseph R. Graney. "CHARACTERIZING UTICA SHALE DEPOSITIONAL PROCESSES USING PORTABLE XRF ANALYSES AND POSITIVE MATRIX FACTORIZATION." In GSA Annual Meeting in Seattle, Washington, USA - 2017. Geological Society of America, 2017. http://dx.doi.org/10.1130/abs/2017am-306930.

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Lahat, Dana, and Cedric Fevotte. "Positive Semidefinite Matrix Factorization: A Link to Phase Retrieval And A Block Gradient Algorithm." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053938.

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"Source Apportionment of Volatile Organic Compounds in SIDCO Kurichi Area Using Positive Matrix Factorization." In 2nd International Conference on Advanced Research in Applied Science and Engineering. GLOBALKS, 2020. http://dx.doi.org/10.33422/2nd.rase.2020.03.98.

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