Semi-supervised discriminate analysis method with locality sensitive
Weiguo He · Jisuanji gongcheng yu sheji · 2010
To solve high-dimensional feature in the pattern classification problems,a data dimensionality reduction method using semi-supervised learning is presented,called locality sensitive semi-supervised discriminate analysis(LSSDA).By discovering the local manifold structure for discriminant analysis,LSSDA finds a projection which minimizes the with-class distance while maximize the between-class distance.The unlabeled data is used as regulatory factors during the optimize process.Extensive experimental results on face recognition database and action recognition database demonstrate the proposed algorithm has an encouraging recognition performation.