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- W4297625631 abstract "Nonnegative Matrix Factorization (NMF) has been a popular representation method for pattern classification problem. It tries to decompose a nonnegative matrix of data samples as the product of a nonnegative basic matrix and a nonnegative coefficient matrix, and the coefficient matrix is used as the new representation. However, traditional NMF methods ignore the class labels of the data samples. In this paper, we proposed a supervised novel NMF algorithm to improve the discriminative ability of the new representation. Using the class labels, we separate all the data sample pairs into within-class pairs and between-class pairs. To improve the discriminate ability of the new NMF representations, we hope that the maximum distance of the within-class pairs in the new NMF space could be minimized, while the minimum distance of the between-class pairs pairs could be maximized. With this criterion, we construct an objective function and optimize it with regard to basic and coefficient matrices and slack variables alternatively, resulting in a iterative algorithm." @default.
- W4297625631 created "2022-09-30" @default.
- W4297625631 creator A5039919016 @default.
- W4297625631 date "2013-12-05" @default.
- W4297625631 modified "2023-09-30" @default.
- W4297625631 title "Max-Min Distance Nonnegative Matrix Factorization" @default.
- W4297625631 doi "https://doi.org/10.48550/arxiv.1312.1613" @default.
- W4297625631 hasPublicationYear "2013" @default.
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