Supervised feature selection via matrix factorization based on singular value decomposition-Reference-Cited by-同舟云学术

Supervised feature selection via matrix factorization based on singular value decomposition

Published:2019-02 Issue: Volume:185 Page:105-113
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Zare Masoumeh,Eftekhari Mahdi,Aghamollaei Gholamreza

Funder

Shahid Bahonar University of Kerman, Iran

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference28 articles.

1. Granular fuzzy possibilistic C-means clustering approach to DNA microarray problem;Truong;Knowl. Base Syst.,2017

2. Informative gene discovery for cancer classification from microarray expression data;Ng;IEEE Workshop on Machine Learning for Signal Processing,2005

3. Supervised feature subset selection with ordinal optimization;Feng;Knowl. Base Syst.,2013

4. l2,1 -norm regularized discriminative feature selection for unsupervised learning;Yang,2011

5. Advancing Feature Selection Research, Technical Report;Zhao,2011

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Subspace learning for feature selection via rank revealing QR factorization: Fast feature selection;Expert Systems with Applications;2024-12

2. Dual-dual subspace learning with low-rank consideration for feature selection;Physica A: Statistical Mechanics and its Applications;2024-10

3. Subspace learning via Hessian regularized latent representation learning with $${l}_{2,0}$$-norm constraint: unsupervised feature selection;International Journal of Machine Learning and Cybernetics;2024-06-21

4. Dual regularized subspace learning using adaptive graph learning and rank constraint: Unsupervised feature selection on gene expression microarray datasets;Computers in Biology and Medicine;2023-12

5. A tutorial-based survey on feature selection: Recent advancements on feature selection;Engineering Applications of Artificial Intelligence;2023-11