Kernel Sparsity Preserving Projections and Its Application to Biometrics
Wankou Yang · Dianzi xuebao · 2013
Sparse representation coefficient contains strong discriminant information and sparsity preserving projections extracts features by sparse representation coefficient.This paper obtains kernel sparse representation coefficient in the high dimensional space by kernel method and use kernel sparse representation coefficient to construct adjacency matrix,then propose kernel sparsity preserving projections.Kernel sparse representation coefficient contains stronger discriminant information than sparse representation coefficient;therefore,KSPP could extract more efficient features than SPP.KSPP achieves good results in biometrics experiments of several databases.