A Survey on Nonconvex Regularization-Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

A Survey on Nonconvex Regularization-Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning. To achieve sparsity and/or low-rankness inducing, the &#x2113;<sub>1</sub> norm and nuclear norm are of the most...

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Bibliographic Details
Main Authors: Fei Wen, Lei Chu, Peilin Liu, Robert C. Qiu
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Sparse
low-rank
nonconvex
compressive sensing
regression
covariance matrix estimation
Online Access:https://ieeexplore.ieee.org/document/8531588/

Internet

https://ieeexplore.ieee.org/document/8531588/

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