Learning the kernel matrix by maximizing a KFD-based class separability criterion

Authors:
Dit-Yan Yeung;Hong Chang;Guang Dai
Affiliations:
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;-;Xerox Research Centre Europe, 6 chemin de Maupertuis, 38240 Meylan, France
Venue:
Pattern Recognition
Year:
2007

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Abstract

The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. In this paper, we propose a novel method for learning the kernel matrix based on maximizing a class separability criterion that is similar to those used by linear discriminant analysis (LDA) and kernel Fisher discriminant (KFD). It is interesting to note that optimizing this criterion function does not require inverting the possibly singular within-class scatter matrix which is a computational problem encountered by many LDA and KFD methods. We have conducted experiments on both synthetic data and real-world data from UCI and FERET, showing that our method consistently outperforms some previous kernel learning methods.