Relevant bibliographies by topics / Data Dimensionality Reduction

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

Academic literature on the topic 'Data Dimensionality Reduction'

Author: Grafiati
Published: 5 September 2021
Last updated: 11 March 2023

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Journal articles on the topic "Data Dimensionality Reduction"

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K., Ashfaq Ahmed, and Dr Shaheda Akthar. "Ridge Regression based Missing Data Estimation with Dimensionality Reduction: Microarray Gene Expression Data." Webology 19, no. 1 (January 20, 2022): 4113–28. http://dx.doi.org/10.14704/web/v19i1/web19271.

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Data is considered to be the important element in the field of Data Science and Machine Learning. Performance of Machine Learning and Data Mining algorithms greatly influenced by the characteristics of data and data with missing values. Performance of all these Machine Learning algorithms greatly improved and they can give accurate results when the data is in full without missing values. So before applying these algorithms; dataset and its missing values are completely filled. To impute these missing values in the dataset there are numerous methods were proposed. In this paper we used micro array gene expression dataset; by introducing various percentages of missing values a new methodology is proposed to impute these missing values in the data set. The nature of micro array gene expression dataset is huge in dimensionality, so at first, we used recursive feature elimination method to select the best features which contributes much for model was selected then we apply the Ridge Regression for imputation. Imputations with other methods are compared. We evaluate the performance of all models by using the metrics like MSE, MAE, R-square. To select the best model in the set of models we used Normalized Criteria Distance (NCD) to rank the models under proposed metrics. The model with least NCD rank selected as the best model among other models, in our paper proposed model has got the lowest value among other models and considered to be the best model among other models.
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K, Bhargavi. "Data Dimensionality Reduction Techniques : Review." International Journal of Engineering Technology and Management Sciences 4, no. 4 (July 28, 2020): 62–65. http://dx.doi.org/10.46647/ijetms.2020.v04i04.010.

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Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data. Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.
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Nagabhushan, P., K. Chidananda Gowda, and Edwin Diday. "Dimensionality reduction of symbolic data." Pattern Recognition Letters 16, no. 2 (February 1995): 219–23. http://dx.doi.org/10.1016/0167-8655(94)00085-h.

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Mahadev, Preeti, and P. Nagabhushan. "Incremental Dimensionality Reduction in Hyperspectral Data." International Journal of Computer Applications 163, no. 7 (April 17, 2017): 21–34. http://dx.doi.org/10.5120/ijca2017913575.

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Sanguinetti, Guido. "Dimensionality Reduction of Clustered Data Sets." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 3 (March 2008): 535–40. http://dx.doi.org/10.1109/tpami.2007.70819.

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Villalón, Elena. "High-Dimensionality Data Reduction with Java." Computing in Science & Engineering 10, no. 5 (September 2008): 64–69. http://dx.doi.org/10.1109/mcse.2008.134.

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., Smita J. Khelukar. "HIGH DIMENSIONALITY REDUCTION ON GRAPHICAL DATA." International Journal of Research in Engineering and Technology 04, no. 11 (November 25, 2015): 177–79. http://dx.doi.org/10.15623/ijret.2015.0411029.

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Gámez, A. J., C. S. Zhou, A. Timmermann, and J. Kurths. "Nonlinear dimensionality reduction in climate data." Nonlinear Processes in Geophysics 11, no. 3 (September 13, 2004): 393–98. http://dx.doi.org/10.5194/npg-11-393-2004.

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Abstract. Linear methods of dimensionality reduction are useful tools for handling and interpreting high dimensional data. However, the cumulative variance explained by each of the subspaces in which the data space is decomposed may show a slow convergence that makes the selection of a proper minimum number of subspaces for successfully representing the variability of the process ambiguous. The use of nonlinear methods can improve the embedding of multivariate data into lower dimensional manifolds. In this article, a nonlinear method for dimensionality reduction, Isomap, is applied to the sea surface temperature and thermocline data in the tropical Pacific Ocean, where the El Niño-Southern Oscillation (ENSO) phenomenon and the annual cycle phenomena interact. Isomap gives a more accurate description of the manifold dimensionality of the physical system. The knowledge of the minimum number of dimensions is expected to improve the development of low dimensional models for understanding and predicting ENSO.
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Gisbrecht, Andrej, and Barbara Hammer. "Data visualization by nonlinear dimensionality reduction." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5, no. 2 (February 23, 2015): 51–73. http://dx.doi.org/10.1002/widm.1147.

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HE, GUANGHUI, ZHAOWEI SHANG, and HENGXIN CHEN. "DISTANCE-RATIO LEARNING FOR DATA VISUALIZATION." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 06 (November 2012): 1250055. http://dx.doi.org/10.1142/s0219691312500555.

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Most dimensionality reduction methods depend significantly on the distance measure used to compute distances between different examples. Therefore, a good distance metric is essential to many dimensionality reduction algorithms. In this paper, we present a new dimensionality reduction method for data visualization, called Distance-ratio Preserving Embedding (DrPE), which preserves the ratio between the pairwise distances. It is achieved by minimizing the mismatch between the distance ratios derived from input and output space. The proposed method can preserve the relational structures among points of the input space. Extensive visualization experiments compared with existing dimensionality reduction algorithms demonstrate the effectiveness of our proposed method.
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Dissertations / Theses on the topic "Data Dimensionality Reduction"

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Vamulapalli, Harika Rao. "On Dimensionality Reduction of Data." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1211.

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Random projection method is one of the important tools for the dimensionality reduction of data which can be made efficient with strong error guarantees. In this thesis, we focus on linear transforms of high dimensional data to the low dimensional space satisfying the Johnson-Lindenstrauss lemma. In addition, we also prove some theoretical results relating to the projections that are of interest when applying them in practical applications. We show how the technique can be applied to synthetic data with probabilistic guarantee on the pairwise distance. The connection between dimensionality reduction and compressed sensing is also discussed.
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Widemann, David P. "Dimensionality reduction for hyperspectral data." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8448.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Baldiwala, Aliakbar. "Dimensionality Reduction for Commercial Vehicle Fleet Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38330.

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A variety of new features have been added in the present-day vehicles like a pre-crash warning, the vehicle to vehicle communication, semi-autonomous driving systems, telematics, drive by wire. They demand very high bandwidth from in-vehicle networks. Various electronic control units present inside the automotive transmit useful information via automotive multiplexing. Automotive multiplexing allows sharing information among various intelligent modules inside an automotive electronic system. Optimum functionality is achieved by transmitting this data in real time. The high bandwidth and high-speed requirement can be achieved either by using multiple buses or by implementing higher bandwidth. But, by doing so the cost of the network and the complexity of the wiring in the vehicle increases. Another option is to implement higher layer protocol which can reduce the amount of data transferred by using data reduction (DR) techniques, thus reducing the bandwidth usage. The implementation cost is minimal as only the changes are required in the software and not in hardware. In our work, we present a new data reduction algorithm termed as “Comprehensive Data Reduction (CDR)” algorithm. The proposed algorithm is used for minimization of the bus utilization of CAN bus for a future vehicle. The reduction in the busload was efficiently made by compressing the parameters; thus, more number of messages and lower priority messages can be efficiently sent on the CAN bus. The proposed work also presents a performance analysis of proposed algorithm with the boundary of fifteen compression algorithm, and Compression area selection algorithms (Existing Data Reduction Algorithm). The results of the analysis show that proposed CDR algorithm provides better data reduction compared to earlier proposed algorithms. The promising results were obtained in terms of reduction in bus utilization, compression efficiency, and percent peak load of CAN bus. This Reduction in the bus utilization permits to utilize a larger number of network nodes (ECU’s) in the existing system without increasing the overall cost of the system. The proposed algorithm has been developed for automotive environment, but it can also be utilized in any applications where extensive information transmission among various control units is carried out via a multiplexing bus.
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DWIVEDI, SAURABH. "DIMENSIONALITY REDUCTION FOR DATA DRIVEN PROCESS MODELING." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069770129.

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XU, NUO. "AGGRESSIVE DIMENSIONALITY REDUCTION FOR DATA-DRIVEN MODELING." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178640357.

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Law, Hiu Chung. "Clustering, dimensionality reduction, and side information." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (Ph. D.)--Michigan State University. Dept. of Computer Science & Engineering, 2006.
Title from PDF t.p. (viewed on June 19, 2009) Includes bibliographical references (p. 296-317). Also issued in print.
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Ross, Ian. "Nonlinear dimensionality reduction methods in climate data analysis." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492479.

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Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These hnear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three such techniques to the study of El Niño/Southern Oscillation variability in tropical Pacific sea surface temperatures and thermocline depth, comparing observational data with simulations from coupled atmosphere-ocean general circulation models from the CMIP3 multi-model ensemble.
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Ray, Sujan. "Dimensionality Reduction in Healthcare Data Analysis on Cloud Platform." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161375080072697.

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Ha, Sook Shin. "Dimensionality Reduction, Feature Selection and Visualization of Biological Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77169.

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Due to the high dimensionality of most biological data, it is a difficult task to directly analyze, model and visualize the data to gain biological insight. Thus, dimensionality reduction becomes an imperative pre-processing step in analyzing and visualizing high-dimensional biological data. Two major approaches to dimensionality reduction in genomic analysis and biomarker identification studies are: Feature extraction, creating new features by combining existing ones based on a mapping technique; and feature selection, choosing an optimal subset of all features based on an objective function. In this dissertation, we show how our innovative reduction schemes effectively reduce the dimensionality of DNA gene expression data to extract biologically interpretable and relevant features which result in enhancing the biomarker identification process. To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) subtypes classification, we extract molecular features from MD microarray data by constructing sub-networks using a novel integrative scheme which utilizes protein-protein interaction (PPI) network, functional gene sets information and mRNA profiling data. The workflow includes three major steps: First, by combining PPI network structure and gene-gene co-expression relationship into a new distance metric, we apply affinity propagation clustering (APC) to build gene sub-networks; secondly, we further incorporate functional gene sets knowledge to complement the physical interaction information; finally, based on the constructed sub-network and gene set features, we apply multi-class support vector machine (MSVM) for MD sub-type classification and highlight the biomarkers contributing to the sub-type prediction. The experimental results show that our scheme could construct sub-networks that are more relevant to MD than those constructed by the conventional approach. Furthermore, our integrative strategy substantially improved the prediction accuracy, especially for those ‘hard-to-classify' sub-types. Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proven incorrect and applying uniform weight in the pathway analysis may not be an adequate approach for tasks like molecular classification of diseases, as genes in a functional group may have different differential power. Hence, we propose to use different weights for the pathway analysis which resulted in the development of four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight. To help us understand our MD expression data better and derive scientific insight from it, we have explored a suite of visualization tools. Particularly, for selected top performing MD sub-networks, we displayed the network view using Cytoscape; functional annotations using IPA and DAVID functional analysis tools; expression pattern using heat-map and parallel coordinates plot; and MD associated pathways using KEGG pathway diagrams. We also performed weighted MD pathway analysis, and identified overlapping sub-networks across different weight schemes and different MD subtypes using Venn Diagrams, which resulted in the identification of a new sub-network significantly associated with MD. All those graphically displayed data and information helped us understand our MD data and the MD subtypes better, resulting in the identification of several potentially MD associated biomarker pathways and genes.
Ph. D.
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Di, Ciaccio Lucio. "Feature selection and dimensionality reduction for supervised data analysis." Thesis, Massachusetts Institute of Technology, 2016. https://hdl.handle.net/1721.1/122827.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 103-106).
by Lucio Di Ciaccio.
S.M.
S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
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Books on the topic "Data Dimensionality Reduction"

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Garzon, Max, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, and Lih-Yuan Deng, eds. Dimensionality Reduction in Data Science. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9.

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Wang, Jianzhong. Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27497-8.

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service), SpringerLink (Online, ed. Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Kramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Barrett, Philip James. Exploratory database visualisation: The application & assessment of data and dimensionality reduction. Birmingham: Aston University. Department of Computer Science and Applied Mathematics, 1995.

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Geometric data analysis: An empirical approach to dimensionality reduction and the study of patterns. New York: Wiley, 2001.

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Nonlinear Dimensionality Reduction. Springer New York, 2010.

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Dimensionality Reduction in Data Science. Springer International Publishing AG, 2022.

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Multilabel Dimensionality Reduction. CRC Press, 2012.

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Ye, Jieping, Shuiwang Ji, and Liang Sun. Multi-Label Dimensionality Reduction. Taylor & Francis Group, 2014.

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Book chapters on the topic "Data Dimensionality Reduction"

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Sarang, Poornachandra. "Dimensionality Reduction." In Thinking Data Science, 19–52. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-02363-7_2.

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Mathar, Rudolf, Gholamreza Alirezaei, Emilio Balda, and Arash Behboodi. "Dimensionality Reduction." In Fundamentals of Data Analytics, 45–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56831-3_4.

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Chepushtanova, Sofya, Elin Farnell, Eric Kehoe, Michael Kirby, and Henry Kvinge. "Dimensionality Reduction." In Data Science for Mathematicians, 291–337. First edition. | Boca Raton, FL : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429398292-7.

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Dinov, Ivo D. "Dimensionality Reduction." In Data Science and Predictive Analytics, 233–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72347-1_6.

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Durstewitz, Daniel. "Dimensionality Reduction." In Advanced Data Analysis in Neuroscience, 105–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59976-2_6.

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Oskolkov, Nikolay. "Dimensionality Reduction." In Applied Data Science in Tourism, 151–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88389-8_9.

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Phillips, Jeff M. "Dimensionality Reduction." In Mathematical Foundations for Data Analysis, 143–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62341-8_7.

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Vlachos, Michail. "Dimensionality Reduction." In Encyclopedia of Machine Learning and Data Mining, 1–8. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_71-1.

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Vlachos, Michail. "Dimensionality Reduction." In Encyclopedia of Machine Learning and Data Mining, 354–61. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_71.

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Suthaharan, Shan. "Dimensionality Reduction." In Machine Learning Models and Algorithms for Big Data Classification, 329–55. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_14.

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Conference papers on the topic "Data Dimensionality Reduction"

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Bunte, Kerstin, Michael Biehl, and Barbara Hammer. "Dimensionality reduction mappings." In 2011 Ieee Symposium On Computational Intelligence And Data Mining - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/cidm.2011.5949443.

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Bingham, Ella, Aristides Gionis, Niina Haiminen, Heli Hiisilä, Heikki Mannila, and Evimaria Terzi. "Segmentation and dimensionality reduction." In Proceedings of the 2006 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.33.

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Zhang, Daoqiang, Zhi-Hua Zhou, and Songcan Chen. "Semi-Supervised Dimensionality Reduction." In Proceedings of the 2007 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2007. http://dx.doi.org/10.1137/1.9781611972771.73.

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Penalver, M., F. Del Frate, M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza. "Onboard payload-data dimensionality reduction." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127069.

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Wei, Jia, Jiabing Wang, Qianli Ma, and Xuan Wang. "Adaptive Semi-Supervised Dimensionality Reduction." In 2014 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2014. http://dx.doi.org/10.1109/icdmw.2014.20.

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Pratap, Rameshwar, Raghav Kulkarni, and Ishan Sohony. "Efficient Dimensionality Reduction for Sparse Binary Data." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622338.

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Aggarwal, Charu C. "The Generalized Dimensionality Reduction Problem." In Proceedings of the 2010 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611972801.53.

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Al-Husain, Luluah, and Alaaeldin M. Hafez. "Dimensionality reduction approach for genotypic data." In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2015. http://dx.doi.org/10.1109/cibcb.2015.7300305.

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Tsai, Flora S., and Kap Luk Chan. "Dimensionality reduction techniques for data exploration." In 2007 6th International Conference on Information, Communications & Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icics.2007.4449863.

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Agrawal, Neelam, and Kesri Verma. "Dimensionality Reduction on Hyperspectral Data Set." In 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T). IEEE, 2020. http://dx.doi.org/10.1109/icpc2t48082.2020.9071461.

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Reports on the topic "Data Dimensionality Reduction"

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Nichols, Jonathan M., Frank Bucholtz, and Joseph V. Michalowicz. Intelligent Data Fusion Using Sparse Representations and Nonlinear Dimensionality Reduction. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada507109.

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Ho, Tu Bao. Methods of Sparse Modeling and Dimensionality Reduction to Deal with Big Data. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada623178.

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Bucholtz, Frank, Jonathan M. Nichols, Michael D. Duncan, and Leslie N. Smith. The Feasibility of Nonlinear Dimensionality Reduction for the Rapid Analysis of Persistent Surveillance Data, including the Detection of IED Placement Activity. Fort Belvoir, VA: Defense Technical Information Center, October 2008. http://dx.doi.org/10.21236/ada488142.

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.
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