Discriminatively Regularized Spectral Regression
Abstract：Spectral Regression is a regularized method for dimensionality reduction.It casts the problem of learning an embedding function into a regression framework,which avoids eigen-decomposition of dense matrices.However,the intra-class information attract more attentions in constructive graph of SR in stead of the critical inter-class information.To address this issue,a novel algorithms for dimensionality reduction are presented,called Discriminatively Regularized Spectral Regression（DRSR） method.DRSR embeds the discriminative information as well as the manifold structures into the regularization term,which aims to retain the intraclass compactness and connects each data point with its neighboring points of the same class,while characterizes the interclass separability and connects the marginal points.The feasibility and effectiveness of the proposed method is then verified on two popular databases（Yale and wine） with promising results.
Keywords：Spectral RegressionDimensionality reductionRegularization method
Publication Date：2011-01-01
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：7( 28-33,80 )