Journal
PATTERN RECOGNITION LETTERS
Volume 34, Issue 5, Pages 545-551Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2012.11.006
Keywords
Heteroscedasticity problem; Super-class; Super-class Discriminant Analysis; Divide and conquer
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Funding
- Natural Science Foundation of China [61272321]
- Zhejiang Provincial Natural Science Foundation of China [Y1101269]
- Grand Program of Zhejiang Province SU Department [2008C14063]
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The heteroscedasti city problem is a great challenge in pattern recognition, particularly in statistics-based methods. The traditional method that is mainly used to solve this problem is heteroscedastic Discriminant Analysis. In this study, we propose a novel solution to the problem, called Super-class Discriminant Analysis (SCDA). Our method uses the divide and conquer methodology to partition the heteroscedastic dataset into super-classes with reduced heteroscedasticity and models them separately. Theoretically, a super-class should contain a set of classes having the same within-class variation. In practice, a heteroscedastic dataset can be coarsely divided into several super-classes based on certain semantic criteria such as gender or race. We evaluate our method with toy data and three real-world datasets, which can be divided into super-classes according to gender and race. Experimental results indicate that the proposed method can effectively resolve the problem of heteroscedasticity. (C) 2012 Elsevier B.V. All rights reserved.
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