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 cluster-size-based scaling improves the clustering by increasing the separability of close clusters, especially when they are of disparate size.
23 citations
TL;DR: This paper reviews the literature related to developing and improving MTS theory, and presents and analyzes the research results in terms of MD, SNR, Mahalanobis Space (MS), feature selection, threshold, multi-class MTS, and comparison with other methods.
23 citations
TL;DR: The model has the advantage that it conveniently quantifies the qualitative indices, and it can integrate the data source information to improve the multi-objective performance indices, so that it is very useful to apply multi-source data and prior knowledge to multi- objective optimization of the automatic train operation control system.
Abstract: The automatic train operation which integrates knowledge-based intelligent algorithm to develop safe and efficient control system has become one of the most important developing directions in the field of railway transit equipment. Multi-objective optimization is a strictly incompatible problem, and such contradiction is one of the main reasons that lead to the best multi-objective optimization difficult to achieve. In this paper, the multi-objective optimization feature information is transformed into the association function first, and then the matter-element theory is introduced to establish models for the speed trajectory to achieve the multi-objective optimization to fuse knowledge-based safety requirement constrained condition. Performance indices weighting of different performance in different stages are determined with the Hierarchical Mahalanobis distance method, and the decision speeds are calculated with goodness evaluation method. Taking Shanghai Railway Transit Equipment in China as a case study, this paper selected the multi-objective performance indices including passenger comfort, running stability, energy efficiency, and parking accuracy as objectives to support the decision-making. The multi-objective performance indices are evaluated by a field investigation and simulation. The test result shows that the comfort level, running stability, energy saving property, and parking accuracy are better than those derived by the traditional control algorithm. It indicates that the model has the advantage that it conveniently quantifies the qualitative indices, and it can integrate the data source information to improve the multi-objective performance indices, so that it is very useful to apply multi-source data and prior knowledge to multi-objective optimization of the automatic train operation control system.
23 citations
21 May 1999
TL;DR: It was shown that performing a new feature selection for each different set of false and true positives is essential and the overall best FROC performance after secondary classification is obtained by varying sensitivity levels in both the first and second step.
Abstract: In this study it is shown that the performance of a statistical method for detection of microcalcification clusters in digital mammograms, can be improved substantially by using a second step of classification. During this second step, detected clusters are automatically classified into true positive and false positive detected clusters. For classification the k-nearest neighbor method was used in a leave-one-patient-out procedure. The sensitivity level of the method was adjusted both in the first detection step as in the second classification step. The Mahalanobis distance was used as criterion in the sequential forward selection procedure for selection of features. This primary feature selection method was combined with a classification performance criterion for the final feature selection. By applying the initial detection at various levels of sensitivity, various sets of false and true positive detected clusters were created. At each of these sets the classification ca be performed. Results show that the overall best FROC performance after secondary classification is obtained by varying sensitivity levels in both the first and second step. Furthermore, it was shown that performing a new feature selection for each different set of false and true positives is essential. A large database of 245 digitized mammograms with 341 clusters was used for evaluation of the method.
23 citations
TL;DR: A novel multivariate signal denoising method that computes Mahalanobis distance measure at multiple data scales obtained from multivariate empirical mode decomposition (MEMD) algorithm is presented and it is proved that the proposed method is able to incorporate inherent correlation between multiple data channels in the Denoising process.
Abstract: A novel multivariate signal denoising method is presented that computes Mahalanobis distance measure at multiple data scales obtained from multivariate empirical mode decomposition (MEMD) algorithm. That enables joint multichannel data denoising directly in multidimensional space $\mathcal {R}^N$ where input signal resides, by employing interval thresholding on multiple data scales in $\mathcal {R}^N$ . We provide theoretical justification of using Mahalanobis distance at multiple scales obtained from MEMD and prove that the proposed method is able to incorporate inherent correlation between multiple data channels in the denoising process. The performance of the proposed method is verified on a range of synthetic and real world signals.
23 citations