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Aimed at solving the problem that traditional clustering methods are vulnerable to the sparsity feature of the high dimensional data, a spectral clustering algorithm is proposed based on K-SVD dictionary learning. The algorithm firstly learns a dictionary by K-SVD and obtains sparse representation coefficients of all data samples in the dictionary by l1 sparse optimization. Then the similarity matrix...
Non-negative matrix factorization (NMF) is useful in finding basis information of non-negative data. It is a new dimension reduction method. In this paper, we modified the original nonnegative matrix factorization in order to extract many basis vectors for each sample cluster. The primary idea is to extend the original NMF through incorporating the latent semantic space constraints inside the NMF...
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