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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,...
Dimensionality reduction and incremental learning have recently received broad attention in many applications of data mining, pattern recognition, and information retrieval. Inspired by the concept of manifold learning, many discriminant embedding techniques have been introduced to seek low-dimensional discriminative manifold structure in the high-dimensional space for feature reduction and classification...
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...
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