Topic
Mahalanobis distance
About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.
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TL;DR: The proposed nearest-neighbor fuzzy association approach for multitarget tracking in a cluttered environment determines the association between the measurements and the tracks based on a single correlation matrix, thus it highly reduces the computational complexity compared to the joint probabilistic data association filter and the conventional fuzzy logic data association approaches.
32 citations
TL;DR: The main conclusion of this study is that the application of Random Forests, or linear discriminant analysis and logistic regression on the condition that calibration data were first reduced on geographical or environmental information, potentially lead toward better vector distribution models.
31 citations
01 Jan 2005
TL;DR: Some refinements are discussed and also the relation with a recently proposed similar method (Hardin and Rocke, 2004) is discussed.
Abstract: Outlier identification is important in many applications of multivariate analysis. Either because there is some specific interest in finding anomalous observations or as a pre-processing task before the application of some multivariate method, in order to preserve the results from possible harmful effects of those observations. It is also of great interest in discriminant analysis if, when predicting group membership, one wants to have the possibility of labelling an observation as ”does not belong to any of the available groups”. The identification of outliers in multivariate data is usually based on Mahalanobis distance. The use of robust estimates of the mean and the covariance matrix is advised in order to avoid the masking effect (Rousseeuw and von Zomeren, 1990; Rocke and Woodruff, 1996; Becker and Gather, 1999). However, the performance of these rules is still highly dependent of multivariate normality of the bulk of the data. The aim of the method here described is to remove this dependency. The first version of this method appeared in Santos-Pereira and Pires (2002). In this talk we discuss some refinements and also the relation with a recently proposed similar method (Hardin and Rocke, 2004).
31 citations
TL;DR: A structural twin parametric-margin support vector machine (STPMSVM) for classification that focuses on the structural information of the corresponding classes based on the cluster granularity is presented.
Abstract: Twin parametric-margin support vector machine (TPMSVM) determines the more flexible parametric-margin hyperplanes through a pair of quadratic programming problems (QPPs) compared with classical support vector machine (SVM). However, it ignores the prior structural information in data. In this paper, we present a structural twin parametric-margin support vector machine (STPMSVM) for classification. The two optimization problems of STPMSVM focus on the structural information of the corresponding classes based on the cluster granularity, which is vital for designing a good classifier in different real-world problems. Furthermore, two Mahalanobis distances are respectively introduced into its corresponding QPPs based on the structural information. STPMSVM has a special case of TPMSVM when each ellipsoid cluster is a unit ball in a reproducing kernel Hilbert space. Experimental results demonstrate that STPMSVM is often superior in generalization performance to other learning algorithms.
31 citations
TL;DR: In this article, a Mahalanobis distance-based damage detection method is studied and compared to the well-known subspace-based approach in the context of two large case studies, in which the joint features of the methods are concluded in a control chart in an attempt to enhance the resolution of the damage detection.
31 citations