Relevant bibliographies by topics / Mahalanobis squared distance

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  2. Dissertations / Theses
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Academic literature on the topic 'Mahalanobis squared distance'

Author: Grafiati
Published: 28 May 2022
Last updated: 30 July 2025

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Journal articles on the topic "Mahalanobis squared distance"

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Etherington, Thomas R. "Mahalanobis distances and ecological niche modelling: correcting a chi-squared probability error." PeerJ 7 (April 2, 2019): e6678. http://dx.doi.org/10.7717/peerj.6678.

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The Mahalanobis distance is a statistical technique that can be used to measure how distant a point is from the centre of a multivariate normal distribution. By measuring Mahalanobis distances in environmental space ecologists have also used the technique to model: ecological niches, habitat suitability, species distributions, and resource selection functions. Unfortunately, the original description of the Mahalanobis distance technique for ecological modelling contained an error describing how Mahalanobis distances could be converted into probabilities using a chi-squared distribution. This e
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Nikita, Efthymia, and Panos Nikitas. "Simulation methods for squared Euclidean and Mahalanobis type distances for multivariate data and their application in assessing the uncertainty in hierarchical clustering." Journal of Statistical Computation and Simulation 92, no. 11 (2022): 2403–24. https://doi.org/10.1080/00949655.2022.2030338.

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This paper extends the Suzuki and Shimodora method [Suzuki R, Shimodora H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics. 2006;22:1540–1542] for assessing uncertainty in hierarchical cluster analysis to multivariate datasets and examines the reliability of the simulated cluster probabilities in relation to the simulation method adopted. The extension is applied to squared Euclidean and Mahalanobis-type distances and employs three simulation methods, the Monte-Carlo and bootstrap methods, and a new proposed method, the distance distribution
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Mukhopadhaya, Sayan, Anil Kumar та Alfred Stein. "FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels". Remote Sensing 10, № 11 (2018): 1707. http://dx.doi.org/10.3390/rs10111707.

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In this paper, the fuzzy c-means (FCM) classifier has been studied with 12 similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray–Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference, Euclidean, Mahalanobis, diagonal Mahalanobis and normalised squared Euclidean distance. Both single and composite modes were used with a varying weight constant (m*) and also at different α-cuts. The two best single measures obtained were combined to study the effect of composite measures on the datasets used. An image
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Nguyen, Theanh, Tommy HT Chan, and David P. Thambiratnam. "Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance." Structural Health Monitoring: An International Journal 13, no. 4 (2014): 461–72. http://dx.doi.org/10.1177/1475921714521270.

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Garrido-Varo, A., J. Garcia-Olmo, and T. Fearn. "A note on Mahalanobis and related distance measures in WinISI and The Unscrambler." Journal of Near Infrared Spectroscopy 27, no. 4 (2019): 253–58. http://dx.doi.org/10.1177/0967033519848296.

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In identifying spectral outliers in near infrared calibration it is common to use a distance measure that is related to Mahalanobis distance. However, different software packages tend to use different variants, which lead to a translation problem if more than one package is used. Here the relationships between squared Mahalanobis distance D2, the GH distance of WinISI, and the T2 and leverage (L) statistics of The Unscrambler are established as D2 = T2 ≈ L × n ≈ GH × k, where n and k are the numbers of samples and variables, respectively, in the set of spectral data used to establish the dista
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Sarmadi, Hassan, Alireza Entezami, and Mohammadhassan Daneshvar Khorram. "Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance." Journal of Vibration and Control 26, no. 11-12 (2019): 1012–27. http://dx.doi.org/10.1177/1077546319891306.

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Damage localization of damaged structures is an important issue in structural health monitoring. In data-based methods based on statistical pattern recognition, it is necessary to extract meaningful features from measured vibration signals and utilize a reliable statistical technique for locating damage. One of the challenging issues is to extract reliable features from non-stationary vibration signals caused by ambient excitation sources. This article proposes a new energy-based method by using ensemble empirical mode decomposition and Mahalanobis-squared distance to obtain energy-based multi
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Iida, Takahisa, Takahiro Fukushima, and Nobuo Shinozaki. "Detection of Abnormality Using Squared Mahalanobis Distance When Both Continuous and Dichotomous Variables Exist." Japanese Journal of Applied Statistics 37, no. 2 (2008): 55–76. http://dx.doi.org/10.5023/jappstat.37.55.

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EADY, MATTHEW, BOSOON PARK, and SUN CHOI. "Rapid and Early Detection of Salmonella Serotypes with Hyperspectral Microscopy and Multivariate Data Analysis†." Journal of Food Protection 78, no. 4 (2015): 668–74. http://dx.doi.org/10.4315/0362-028x.jfp-14-366.

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This study was designed to evaluate hyperspectral microscope images for early and rapid detection of Salmonella serotypes Enteritidis, Heidelberg, Infantis, Kentucky, and Typhimurium at incubation times of 6, 8, 10, 12, and 24 h. Images were collected by an acousto-optical tunable filter hyperspectral microscope imaging system with a metal halide light source measuring 89 contiguous wavelengths every 4 nm between 450 and 800 nm. Pearson correlation values were calculated for incubation times of 8, 10, and 12 h and compared with data for 24 h to evaluate the change in spectral signatures from b
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Dzeverin, Igor, and Maria Ghazali. "Evolutionary Mechanisms Affecting the Multivariate Divergence in Some Myotis Species (Chiroptera, Vespertilionidae)." Evolutionary Biology 37, no. 2-3 (2010): 100–112. https://doi.org/10.5281/zenodo.13470115.

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(Uploaded by Plazi for the Bat Literature Project) Phenotypic evolutionary rates were measured for 27 craniometric characters in 12 extant OTUs from the bat genus Myotis (Chiroptera, Vespertilionidae). Squared Mahalanobis distance was used as a multivariate measure for amount of divergence, and squared Mahalanobis distance weighted by time was used as a measure for the rate of divergence. Estimates for the rates of divergence were found to be consistent with random walk hypothesis. Thus, the divergence in Myotis could be guided by random drift and mutations. The high dispersion in rate estimat
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Dzeverin, Igor, and Maria Ghazali. "Evolutionary Mechanisms Affecting the Multivariate Divergence in Some Myotis Species (Chiroptera, Vespertilionidae)." Evolutionary Biology 37, no. 2-3 (2010): 100–112. https://doi.org/10.5281/zenodo.13470115.

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(Uploaded by Plazi for the Bat Literature Project) Phenotypic evolutionary rates were measured for 27 craniometric characters in 12 extant OTUs from the bat genus Myotis (Chiroptera, Vespertilionidae). Squared Mahalanobis distance was used as a multivariate measure for amount of divergence, and squared Mahalanobis distance weighted by time was used as a measure for the rate of divergence. Estimates for the rates of divergence were found to be consistent with random walk hypothesis. Thus, the divergence in Myotis could be guided by random drift and mutations. The high dispersion in rate estimat
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Dissertations / Theses on the topic "Mahalanobis squared distance"

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Prykhodko, S., N. Prykhodko, L. Makarova, O. Kudin, T. Smykodub, and A. Prykhodko. "Detecting bivariate outliers on the basis of normalizing transformations for non-Gaussian data." Thesis, Sumy State University, 2017. http://essuir.sumdu.edu.ua/handle/123456789/55754.

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The statistical technique for detecting outliers in bivariate non-Gaussian data on the basis of normalizing transformations, prediction ellipse and a test statistic (TS) for the Mahalanobis squared distance (MSD), which has an approximate F distribution, is proposed. Application of the technique is considered for detecting outliers in two bivariate non-Gaussian data sets: the first, actual effort (hours) and size (adjusted function points) from 145 maintenance and development projects, the second, effort (hours) and mass (tonnes) of designed the section of the ship from 188 designs
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Rynešová, Pavlína. "Diskriminační a shluková analýza jako nástroj klasifikace objektů." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-201591.

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Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can be a disordered group of objects organized into several internally homogeneous classes or clusters. Discriminant analysis creates knowledge based on the jurisdiction of existing classes classification rule, which can be then used for classifying units with an unknown group membership. The aim of this thesis is a comparison of discriminant analysis and different methods of cluster analysis. To reflect the distances between objects within each cluster, squeared Euclidean and Mahalanobis distances
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Gardner, Robert Matthew. "A Wide-Area Perspective on Power System Operation and Dynamics." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/26779.

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Classically, wide-area synchronized power system monitoring has been an expensive task requiring significant investment in utility communications infrastructures for the service of relatively few costly sensors. The purpose of this research is to demonstrate the viability of power system monitoring from very low voltage levels (120 V). Challenging the accepted norms in power system monitoring, the document will present the use of inexpensive GPS time synchronized sensors in mass numbers at the distribution level. In the past, such low level monitoring has been overlooked due to a perceived
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, i
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(12271705), John Austin Guthrie. "Robustness of NIR calibrations for assessing fruit quality." Thesis, 2005. https://figshare.com/articles/thesis/Robustness_of_NIR_calibrations_for_assessing_fruit_quality/21454485.

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<p>Predictive models based on near infra-red spectroscopy for the assessment of fruit internal quality attributes must exhibit a degree of robustness across the parameters of variety, district and time to be of practical use in fruit grading. At the time this thesis was initiated, while there were a number of published reports on the development of near infra-red based calibration models for the assessment of internal quality attributes of intact fruit, there were no reports of the reliability ("robustness") of such models across time, cultivars or growing regions. As existing published report
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Book chapters on the topic "Mahalanobis squared distance"

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Huang, Mengdie, and Shuang Li. "Low-Thrust Collision Avoidance Maneuver Design Considering Square Mahalanobis Distance Constraint." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2236-8_47.

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Anderson, Raymond A. "Stats & Maths & Unicorns." In Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0011.

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This chapter covers basic statistical concepts. Most statistics relate to hypothesis testing, and others to variable selection and model fitting. The name is because an exact match between a theoretical and empirical distribution is as rare as a unicorn. (1) Dispersion—measures of random variations—variance and its inflation factor, covariance and correlations {Pearson’s product-moment, Spearman’s rank order}, and the Mahalanobis distance. (2) Goodness-of-fit—do observations match expectations? This applies to both continuous dependent variables {R-squared and adjusted R2} and categorical {Pea
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Conference papers on the topic "Mahalanobis squared distance"

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Xiao-Lin Zou and Xiao-Zhang Liu. "Sample reduction based on kernel squared Mahalanobis distance for support vector machines." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5623208.

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Prykhodko, Sergiy, Natalia Prykhodko, Lidiia Makarova, and Andrii Pukhalevych. "Application of the squared mahalanobis distance for detecting outliers in multivariate non-Gaussian data." In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2018. http://dx.doi.org/10.1109/tcset.2018.8336353.

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Liu, Kai, Lodewijk Brand, Hua Wang, and Feiping Nie. "Learning Robust Distance Metric with Side Information via Ratio Minimization of Orthogonally Constrained L21-Norm Distances." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/417.

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Metric Learning, which aims at learning a distance metric for a given data set, plays an important role in measuring the distance or similarity between data objects. Due to its broad usefulness, it has attracted a lot of interest in machine learning and related areas in the past few decades. This paper proposes to learn the distance metric from the side information in the forms of must-links and cannot-links. Given the pairwise constraints, our goal is to learn a Mahalanobis distance that minimizes the ratio of the distances of the data pairs in the must-links to those in the cannot-links. Dif
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Polli, Eduardo Maria. "Fuel-optimal low-thrust CAM with eclipse constraints." In IV Aerospace PhD-Days. Materials Research Forum LLC, 2024. http://dx.doi.org/10.21741/9781644903193-35.

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Abstract. This works presents an optimal control direct method to design fuel-optimal low-thrust Collision Avoidance Manoeuvres (CAMs), imposing return to nominal conditions and including eclipse constraints. The methodology exploits the Hermite-Simpon integration scheme to impose the dynamics and the Squared Mahalanobis Distance (SMD) to ensure a collision probability lower than a prescribed value. The choice of using a direct method to solve the Optimal Control Problem (OCP) is justified by the inclusion of orbital perturbations.
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WANG, TINGNA, DAVID J. WAGG, ROBERT J. BARTHORPE, and KEITH WORDEN. "ON ROBUSTNESS OF OPTIMAL SENSOR PLACEMENT TO ENVIRONMENTAL VARIATION FOR SHM." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36350.

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One challenge in establishing an effective structural health monitoring (SHM) system is the impact of environmental variability on damage identification. It is therefore, advantageous to consider any environmental effects when conducting sensor placement optimisation (SPO). One approach to this problem is to check the robustness of SPO technique to environmental variations and consider whether it is necessary to take account of these environmental factors as part of the optimisation process. This paper will study the robustness of an SPO method to variations in the ambient temperature of the s
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ENTEZAMI, ALIREZA, and STEFANO MARIANI. "A NON-PARAMETRIC MIXED LEARNING TECHNIQUE FOR MITIGATING ENVIRONMENTAL EFFECTS ON STRUCTURAL MODAL FREQUENCIES." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36927.

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Health monitoring of civil structures via machine learning is a powerful approach to the early detection of any damage pattern. Besides structural damage, also environmental and operational variabilities are known to affect the inherent structural properties. Although the induced variations in the monitored properties are not harmful, their confounding influence can lead to economic and human losses. For these reasons, a novel unsupervised learning strategy is here proposed, aiming to properly account for the environmental effects on the structural modal frequencies. The offered solution is a
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Sousa, Luiz Felipe, Adam Dreyton Ferreira Santos, and João Weyl Albuquerque Costa. "Imputação de dados ausentes através de redes neurais recorrentes no monitoramento de integridade estrutural." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-137.

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Um problema comum em grandes conjuntos de dados é a informação ausente, seja por falha nos sensores de captura, perda no transporte, ou outra situação que culmine com a perda de dados. Diante desta situação, é frequente que a decisão do pesquisador seja desconsiderar os dados ausentes, removê-los do conjunto, no entanto, essa exclusão pode gerar inferências que não são válidas, principalmente se os dados que permanecem na análise são diferentes daqueles que foram excluídos. Para lidar com este problema em conjuntos de dados de monitoramento de integridade estrutural (Structural health monitori
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