Unsupervised feature selection via maximum projection and minimum redundancy-Reference-Cited by-同舟云学术

Unsupervised feature selection via maximum projection and minimum redundancy

Published:2015-02 Issue: Volume:75 Page:19-29
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Wang Shiping,Pedrycz Witold,Zhu Qingxin,Zhu William

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province, China

Science and Technology Key Project of Fujian Province, China

Publisher

Elsevier BV

Subject

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

Reference29 articles.

1. An introduction to variable and feature selection;Guyon;J. Mach. Learn. Res.,2003

2. Y. Yang, J.O. Pedersen, A comparative study on feature selection in text categorization, in: International Conference on Machine Learning, 1997, pp. 412–420.

3. Pattern Classification,2000

4. Q. Gu, Z. Li, J. Han, Generalized fisher score for feature selection, in: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain, 2011, pp. 1–8.

Cited by 65 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. Multi-cluster nonlinear unsupervised feature selection via joint manifold learning and generalized Lasso;Expert Systems with Applications;2024-12

3. Unsupervised feature selection based on minimum-redundant subspace learning with self-weighted adaptive graph;Digital Signal Processing;2024-12

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

5. Unsupervised feature selection based on bipartite graph and low-redundant regularization;Knowledge-Based Systems;2024-10