Dictionary learning for unsupervised feature selection via dual sparse regression-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Dictionary learning for unsupervised feature selection via dual sparse regression

Published:2023-02-11 Issue:15 Volume:53 Page:18840-18856
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Wu Jian-Sheng,Liu Jing-Xin,Wu Jun-Yun,Huang Wei

Funder

Postgraduate Innovation Foundation of Jiangxi Province

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-04480-0.pdf

Reference47 articles.

1. Shang R, Xu K, Shang F, Jiao L (2020) Sparse and low-redundant subspace learning-based dual-graph regularized robust feature selection. Knowl-Based Syst 187:104830. https://doi.org/10.1016/j.knosys.2019.07.001

2. Wang F, Zhu L, Li J, Chen H, Zhang H (2021) Unsupervised soft-label feature selection. Knowl-Based Syst 219:106847. https://doi.org/10.1016/j.knosys.2021.106847

3. Zhou H, Wang X, Zhu R (2022) Feature selection based on mutual information with correlation coefficient. Appl Intell 52(5):5457–5474. https://doi.org/10.1007/s10489-021-02524-x

4. Cai J, Wang S, Guo W (2021) Unsupervised embedded feature learning for deep clustering with stacked sparse auto-encoder. Expert Syst Appl 186:115729. https://doi.org/10.1016/j.eswa.2021.115729

5. Cai J, Fan J, Guo W, Wang S, Zhang Y, Zhang Z (2022) Efficient deep embedded subspace clustering. In: CVPR. https://doi.org/10.1109/CVPR52688.2022.00012, pp 21–30

Cited by 3 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. Joint Cauchy dictionary learning and graph learning for unsupervised feature selection;Engineering Applications of Artificial Intelligence;2024-10

2. Adaptive and flexible $$\ell _1$$-norm graph embedding for unsupervised feature selection;Applied Intelligence;2024-09-02

3. A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation;Applied Intelligence;2023-06-30

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