A supervised multi-view feature selection method based on locally sparse regularization and block computing-Reference-Cited by-同舟云学术

A supervised multi-view feature selection method based on locally sparse regularization and block computing

Published:2022-01 Issue: Volume:582 Page:146-166
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Lin Qiang,Men Min,Yang Liran,Zhong Ping

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference36 articles.

1. Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis;Benavoli;J. Mach. Learn. Res.,2017

2. Multi-view feature selection via nonnegative structured graph learning;Bai;Neurocomputing,2020

3. Distributed optimization and statistical learning via the alternating direction method of multipliers;Boyd;Found. Trends Mach. Learn.,2011

4. Exact top-k feature selection via l2,0-norm constraint;Cai,2013

5. Semi-supervised multi-view maximum entropy discrimination with expectation Laplacian regularization;Chao;Inf. Fusion,2019

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