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	Discriminant analysis on embedded manifold
	Yan, SC ; Zhang, HJ ; Hu, YX ; Zhang, BY ; Cheng, QS
	2004
英文摘要	Previous manifold learning algorithms mainly focus on uncovering the low dimensional geometry structure from a set of samples that lie on or nearly on a manifold in an unsupervised manner. However, the representations from unsupervised learning are not always optimal in discriminating capability. In this paper, a novel algorithm is introduced to conduct discriminant analysis in term of the embedded manifold structure. We propose a novel clustering algorithm, called Intra-Cluster Balanced K-Means (ICBKM), which ensures that there are balanced samples for the classes in a cluster; and the local discriminative features for all clusters are simultaneously calculated by following the global Fisher criterion. Compared to the traditional linear/kemel discriminant analysis algorithms, ours has the following characteristics: 1) it is approximately a locally linear yet globally nonlinear discriminant analyzer; 2) it can be considered a special Kemel-DA with geometry-adaptive-kernel, in contrast to traditional KDA whose kernel is independent to the samples; and 3) its computation and memory cost are reduced a great deal compared to traditional KDA, especially for the cases with large number of samples. It does not need to store the original samples for computing the low dimensional representation for new data. The evaluation on toy problem shows that it is effective in deriving discriminative representations for the problem with nonlinear classification hyperplane. When applied to the face recognition problem, it is shown that, compared with LDA and traditional KDA on YALE and PIE databases, the proposed algorithm significantly outperforms LDA and Mixture LDA, has better accuracy than Kemel-DA with Gaussian Kernel.; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; SCI(E); CPCI-S(ISTP); 1
语种	英语
出处	SCI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/400346]
专题	数学科学学院
推荐引用方式 GB/T 7714	Yan, SC,Zhang, HJ,Hu, YX,et al. Discriminant analysis on embedded manifold. 2004-01-01.

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