Heteroscedastic Max-Min Distance Analysis for Dimensionality Reduction

Max-min distance analysis (MMDA) performs dimensionality reduction by maximizing the minimum pairwise distance between classes in the latent subspace under the homoscedastic assumption, which can address the class separation problem caused by the Fisher criterion but is incapable of tackling heteros...

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Bibliographic Details
Published in:IEEE transactions on image processing 2018-08, Vol.27 (8), p.4052-4065
Main Authors: Su, Bing, Ding, Xiaoqing, Liu, Changsong, Wu, Ying
Format: Article
Language:English
Subjects:
Chernoff distance
class separation problem
Covariance matrices
Dimensionality reduction
Electronic mail
Hidden Markov models
Image processing
max-min distance analysis
Minimization
trace quotient
Training
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Summary:Max-min distance analysis (MMDA) performs dimensionality reduction by maximizing the minimum pairwise distance between classes in the latent subspace under the homoscedastic assumption, which can address the class separation problem caused by the Fisher criterion but is incapable of tackling heteroscedastic data properly. In this paper, we propose two heteroscedastic MMDA (HMMDA) methods to employ the differences of class covariances. Whitened HMMDA extends MMDA by utilizing the Chernoff distance as the separability measure between classes in the whitened space. Orthogonal HMMDA (OHMMDA) incorporates the maximization of the minimal pairwise Chernoff distance and the minimization of class compactness into a trace quotient formulation with an orthogonal constraint of the transformation, which can be solved by bisection search. Two variants of OHMMDA further encode the margin information by using only neighboring samples to construct the intra-class and inter-class scatters. Experiments on several UCI datasets and two face databases demonstrate the effectiveness of the HMMDA methods.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2018.2836312