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Robust and Discriminative Concept Factorization for Image Representation
2015
Proceedings of the 5th ACM on International Conference on Multimedia Retrieval - ICMR '15
Concept Factorization (CF), as a variant of Nonnegative Matrix Factorization (NMF), has been widely used for learning compact representation for images because of its psychological and physiological interpretation of naturally occurring data. And graph regularization has been incorporated into the objective function of CF to exploit the intrinsic low-dimensional manifold structure, leading to better performance. But some shortcomings are shared by existing CF methods. 1) The squared loss used
doi:10.1145/2671188.2749317
dblp:conf/mir/GuoDZL15
fatcat:r5pyljlsz5h65n6xbni5xjj7ke