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期刊论文

A Modified Algorithm for Generalized Discriminant Analysis

邹采荣Wenming Zheng Li Zhao Cairong Zou

Neural Computation 16(2004)1283-1297,-0001,():

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摘要/描述

Generalized discriminant analysis (GDA) is an extension of the classical linear discriminant analysis (LDA) from linear domain to a nonlinear domain via the kernel trick. However, in the previous lgorithm of GDA, the solutions may suffer from the degenerate eigenvalue problem (i.e., several eigenvectors with the same eigenvalue), which makes them not optimal in terms of the discriminant ability. In this article, we propose a modified algorithm for GDA (MGDA) to solve this problem. The MGDA method aims to remove the degeneracy of GDA and find the optimal discriminant solutions, which maximize the between-class scatter in the subspace spanned by the degenerate eigenvectors of GDA. Theoretical analysis and experimental results on the ORL face database show that the MGDA method achieves better performance than the GDA method.

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版权说明:以下全部内容由邹采荣上传于   2005年05月18日 00时30分35秒,版权归本人所有。

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