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Existing matrix factorization based techniques, such as nonnegative matrix factorization and concept factorization, have been widely applied for data representation. In order to make the obtained concepts to be as close to the original data points as possible, one state-of-the-art method called locality constraint concept factorization is put forward, which represent the data by a linear combination...
Recently, manifold regularization with the affinity graph in matrix factorization-related studies, such as dual-graph regularized concept factorization (GCF), have yielded impressive results for clustering. However, due to the noisy and irrelevant features of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic...
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