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Recently, sparse coding has been widely adopted for data representation in real-world applications. In order to consider the geometric structure of data, we propose a novel method, local and global regularized sparse coding (LGSC), for data representation. LGSC not only models the global geometric structure by a global regression regularizer, but also takes into account the manifold structure using...
Matrix factorization methods have been widely applied for data representation. Traditional concept factorization, however, fails to utilize the discriminative structure information and the geometric structure information that can improve the performance in clustering. In this paper, we propose a novel matrix factorization method, called Local Regularization Concept Factorization (LRCF), for image...
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