Robust Low-Rank Matrix Factorization Under General Mixture Noise Distributions

Many computer vision problems can be posed as learning a low-dimensional subspace from high-dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problems using L1-...

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Publié dans:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 25(2016), 10 vom: 02. Okt., Seite 4677-4690
Auteur principal:	Xiangyong Cao (Auteur)
Autres auteurs:	Qian Zhao, Deyu Meng, Yang Chen, Zongben Xu
Format:	Article en ligne
Langue:	English
Publié:	2016
Accès à la collection:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Sujets:	Journal Article