Sparse and low-redundant subspace learning-based dual-graph regularized robust feature selection-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Sparse and low-redundant subspace learning-based dual-graph regularized robust feature selection

Published:2020-01 Issue: Volume:187 Page:104830
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Shang Ronghua,Xu Kaiming,Shang Fanhua,Jiao Licheng

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Key Laboratory Fund, China

Program for Cheung Kong Scholars and Innovative Research Team in University, China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference64 articles.

1. Global and intrinsic geometric structure embedding for unsupervised feature selection;Wan;Expert Syst. Appl.,2018

2. Graph autoencoder-based unsupervised feature selection with broad and local data structure preservation;Feng;Neurocomputing,2018

3. Self-representation based dual-graph regularized feature selection clustering;Shang;Neurocomputing,2016

4. Non-negative spectral learning and sparse regression-based dual-graph regularized feature selection;Shang;IEEE Trans. Cybern.,2018

5. Unsupervised feature selection by combining subspace learning with feature self-representation;Li;Pattern Recognit. Lett.,2018

Cited by 56 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. Unsupervised feature selection method based on dual manifold learning and dual spatial latent representation;Expert Systems with Applications;2024-12

3. Explicit unsupervised feature selection based on structured graph and locally linear embedding;Expert Systems with Applications;2024-12

4. Dual space-based fuzzy graphs and orthogonal basis clustering for unsupervised feature selection;Pattern Recognition;2024-11

5. Non-convex feature selection based on feature correlation representation and dual manifold optimization;Expert Systems with Applications;2024-09

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3