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In order to exploit the informative components hidden in nonnegative matrix factorization, an information theoretic learning method, termed ITNMF, is presented. Different from the existing NMF methods, the proposed method is able to handle the general objective optimization, and takes the conjugate gradient technique to enhance the iterative optimization. To tackle the null matrix factorization problem,...
Inspired by the concept of manifold learning, the discriminant embedding technologies aim to exploit low dimensional discriminant manifold structure in the high dimensional space for dimension reduction and classification. However, such graph embedding framework based techniques usually suffer from the large complexity and small sample size (SSS) problem. To address the problem, we reformulate the...
In this paper, a novel topology preserving non-negative matrix factorization (TPNMF) method is proposed for face recognition. We derive the TPNMF model from original NMF algorithm by preserving local topology structure. The TPNMF is based on minimizing the constraint gradient distance in the high-dimensional space. Compared with L2 distance, the gradient distance is able to reveal latent manifold...
In this paper, we propose a face recognition method called the Laplacian Nonnegative Matrix Factorization. By incorporating Laplacianfaces inside the Nonnegative Matrix Factorization (NMF) decomposition, the goal is to extend the NMF algorithm in order to extract discriminant information by preserving locality information in face subspac. With Laplacian NMF decomposition, it is expected to own Laplacianfaces...
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