广义二维主成分分析

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI:10.1109/IJCNN.2005.1555814

Hui Kong, Xuchun Li, Lei Wang, E. K. Teoh, Jian-gang Wang, R. Venkateswarlu

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引用次数: 90

摘要

J. Yang等人(2004)提出了二维主成分分析(2DPCA)，并证明了其在人脸识别中优于传统主成分分析(PCA)的优点。但迄今为止，还没有理论证明2DPCA优于PCA。本文分析了二维主成分分析的本质，提出了广义二维主成分分析框架(G2DPCA)，从两个方面对原有的二维主成分分析进行了扩展:基于双边投影的2DPCA (B2DPCA)和基于核的2DPCA (K2DPCA)方案。人脸识别实验结果表明了该方法的优异性能。

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Generalized 2D principal component analysis

A two-dimensional principal component analysis (2DPCA) by J. Yang et al. (2004) was proposed and the authors have demonstrated its superiority over the conventional principal component analysis (PCA) in face recognition. But the theoretical proof why 2DPCA is better than PCA has not been given until now. In this paper, the essence of 2DPCA is analyzed and a framework of generalized 2D principal component analysis (G2DPCA) is proposed to extend the original 2DPCA in two perspectives: a bilateral-projection-based 2DPCA (B2DPCA) and a kernel-based 2DPCA (K2DPCA) schemes are introduced. Experimental results in face recognition show its excellent performance.

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Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

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