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	Local fisher discriminant analysis with locally linear embedding affinity matrix
	Zhao, Yue ; Ma, Jinwen
	2013
英文摘要	Fisher Discriminant Analysis (FDA) is a popular method for dimensionality reduction. Local Fisher Discriminant Analysis (LFDA) is an improvement of FDA, which can preserve the local structures of the feature space in multi-class cases. However, the affinity matrix in LFDA cannot reflect the actual interrelationship among all the neighbors for each sample point. In this paper, we propose a new LFDA approach with the affinity matrix being solved by the locally linear embedding (LLE) method to preserve the particular local structures of the specific feature space. Moreover, for nonlinear cases, we extend this new LFDA method to the kernelized version by using the kernel trick. It is demonstrated by the experiments on five real-world datasets that our proposed LFDA methods with LLE affinity matrix are applicable and effective. ? 2013 Springer-Verlag Berlin Heidelberg.; EI; 0
语种	英语
出处	EI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/411791]
专题	数学科学学院
推荐引用方式 GB/T 7714	Zhao, Yue,Ma, Jinwen. Local fisher discriminant analysis with locally linear embedding affinity matrix. 2013-01-01.

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