sparse and low-rank matrix factorization

Wednesday, January 27, 2016 - 4:15pm - 5:10pm
René Vidal (Johns Hopkins University)
Matrix, tensor, and other factorization techniques are used in a wide range of applications and have enjoyed significant empirical success in many fields. However, common to a vast majority of these problems is the significant disadvantage that the associated optimization problems are typically non-convex due to a multilinear form or other convexity destroying transformation.
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