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Maximizing generalized mean of trace ratios for discriminative feature learning
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- Authors
- Jiyong Oh
- Issue Date
- 2025-08
- Citation
- Pattern Recognition Letters, v.194, pp.26-31
- ISSN
- 0167-8655
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- Although linear discriminant analysis (LDA) is effective and efficient, it does not always succeed in obtaining discriminative features. This paper deals with the class separation (CS) problem leading to the failure of LDA. This study first presents a general framework leveraging the generalized mean in which some previous methods to address the CS problem become specific cases. The proposed framework is meaningful because it integrates the previous methods in two different approaches from a single framework. Then, this study proposes a new dimensionality reduction method by employing the trace ratio of between-class scatter and within-class scatter as a dissimilarity measure, which is also a special case of the proposed framework. Classification experiments on five datasets demonstrate that the proposed method is competitive with other state-of-the-art methods.
- KSP Keywords
- Between-class scatter, Discriminative feature learning, General Framework, Generalized Mean, Reduction method, Within-class scatter, dimensionality reduction, dissimilarity measure, linear discriminant analysis(LDA), state-of-The-Art