SparsityPreservingDiscriminant Analysis Based on Spectral Techniques
Abstract：parsity Preserving Discriminant Analysis(SPDA) is a popular graph-based semi-supervised dimensionality reduction(SSDR) algorithm,which does not only contain natural discriminating information by sparse reconstruction relationship of the data sets,but also more applicable to face recognition problem with only a few training sample.However,the computation of SPDA involves eigen-decomposition of the dense matrix which is expensive in both memory and computation.In this paper,we propose an improved SPDA algorithm called Sparsity Preserving Discriminant Analysis based on Spectral Techniques(SSPDA).Specifically,the proposed approach casts discriminant analysis into a regression framework,and then the projection directions can be efficiently computed by related ridge regression,thus eigen-decomposition of the dense matrix is avoid.Experimental results on single training image face recognition demonstrate the effectiveness of the proposed algorithm.
Keywords：semi-supervised discriminant analysisridge regressiondimensionality reductionspectral techniquesface recognition
Publication Date：2012-01-01
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：5( 16-20 )