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Kernel methods and Nonnegative matrix factorization (NMF) are both widely used in data mining and machine learning. The previous one is best known for its capability of transforming data into high dimension feature space, while the latter one is well known for its natural interpretations and good performance. In this paper, we propose a robust kernel NMF approach using L2, 1 norm loss function. Compared...
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