An improved supervised sparsity preserving CCA algorithm based on exponential dimensionality reduction
JIANG Wen
QI Lin
Abstract：An improved supervised sparsity preserving canonical correlation analysis algorithm based on ex-ponential dimensionality reduction was proposed.The problem that the fitting error increased while adding supervised information to the SPCCA was solved by the fusion of the class label information and sample fea-ture.The local manifold structure of the data was realized at the same time.Aimed at the problem of tradi-tional algorithm in dealing with small sample of high-dimensiona sparse signal,index scattering matrix was used to retain effective information while building the non-singular scattering matrix.It overcame the de-fault of effective information losses while using PCA to extract principal features of the scattering matrix. The experimental results on ORL,Yale,AR and FERET face databases showed that the proposed algorithm was better than related canonical correlation analysis methods in recognition effect.
Keywords：canonical correlation analysis(CCA)sparsity preserving projection(SPP)exponential dimen-sionality reductionfeature extractionface recognition
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 93-97 )