Maximum variance sparse mapping

Authors:
Bo Li;Jin Liu;Wenyong Dong
Affiliations:
State Key Lab. of Software Engineering, Wuhan University, Wuhan, China and College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;State Key Lab. of Software Engineering, Wuhan University, Wuhan, China;Computer School, Wuhan University, Wuhan, China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

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Abstract

In this paper, a multiple sub-manifold learning method oriented classification is presented via sparse representation, which is named maximum variance sparse mapping. Based on the assumption that data with the same label locate on a sub-manifold and different class data reside in the corresponding sub-manifolds, the proposed algorithm can construct an objective function which aims to project the original data into a subspace with maximum sub-manifold distance and minimum manifold locality. Moreover, instead of setting the weights between any two points directly or obtaining those by a square optimal problem, the optimal weights in this new algorithm can be approached using L1 minimization. The proposed algorithm is efficient, which can be validated by experiments on some benchmark databases.