A new subspace analysis approach based on laplacianfaces

Authors:
Yan Wu;Ren-Min Gu
Affiliations:
Dept. of Computer Science and Technology, Tongji University, Shanghai, P.R. of China;Dept. of Computer Science and Technology, Tongji University, Shanghai, P.R. of China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

A new subspace analysis approach named ANLBM is proposed based on Laplacianfaces. It uses the discriminant information of training samples by supervised mechanism, enhances within-class local information by an objective function. The objective function is used to construct adjacency graph's weight matrix. In order to avoid the drawback of Laplacianfaces' PCA step, ANLBM uses kernel mapping. ANLBM changes the problem from minimum eigenvalue solution to maximum eigenvalue solution, reduces the redundancy of the computing and increases the precision of the result. The experiments are performed on ORL and Yale databases. Experimental results show that ANLBM has a better performance.