Robust Low-Rank Matrix Factorization Under General Mixture Noise Distributions
نویسندگان
چکیده
منابع مشابه
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-norm and L2-norm losses, which mainly deal with Laplacian and Gaussian noises, respectively. To m...
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Low-rank matrix factorization (MF) is an important technique in data science. The key idea of MF is that there exists latent structures in the data, by uncovering which we could obtain a compressed representation of the data. By factorizing an original matrix to low-rank matrices, MF provides a unified method for dimension reduction, clustering, and matrix completion. In this article we review ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2016
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2016.2593343