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- W2380853780 abstract "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." @default.
- W2380853780 created "2016-06-24" @default.
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- W2380853780 date "2010-01-01" @default.
- W2380853780 modified "2023-09-26" @default.
- W2380853780 title "Semi-supervised discriminate analysis method with locality sensitive" @default.
- W2380853780 hasPublicationYear "2010" @default.
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