Nonsmooth low-rank matrix recovery: methodology, theory and algorithm

Many interesting problems in statistics and machine learning can be written as minx F (x) = f (x) + g(x), where x is the model parameter, f is the loss and g is the regularizer. Examples include regularized regression in high-dimensional feature selection and low-rank matrix/tensor factorization. So...

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Bibliographic Details

Main Authors:	Wei Tu, Peng Liu, Yi Liu, Guodong Li, Bei Jiang, Linglong Kong, Hengshuai Yao, Shangling Jui
Format:	Default Conference proceeding
Published:	2021
Subjects:	Matrix factorization Nonsmooth Low-rank matrix Nesterov’s smoothing Optimization
Online Access:	https://hdl.handle.net/2134/27157296.v1
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