Unsupervised feature selection based on self-representation sparse regression and local similarity preserving-Reference-Cited by-同舟云学术

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Unsupervised feature selection based on self-representation sparse regression and local similarity preserving

Published:2017-12-18 Issue:4 Volume:10 Page:757-770
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Shang Ronghua,Chang Jiangwei,Jiao Licheng,Xue Yu

Funder

the National Natural Science Foundation of China

the National Basic Research Program (973 Program) of China

Program for Cheung Kong Scholars and Innovative Research Team in University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s13042-017-0760-y.pdf

Reference56 articles.

1. Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416

2. Gu B, Sun XM, Sheng VS (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2016.2544779

3. Tian Q, Chen S (2017) Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing 238:286–295

4. Mutch J, Lowe DG (2006) Multiclass object recognition with sparse localized features. In: Proceedings IEEE computer society conference on computer vision pattern recognit, pp 11–18

5. Li Z, Tang J (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343–5355

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