A generalized l2,p-norm regression based feature selection algorithm-Reference-Cited by-同舟云学术

A generalized l2,p-norm regression based feature selection algorithm

Published:2021-09-17 Issue: Volume: Page:1-21
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Zhi X.¹,Liu J.²,Wu S.²,Niu C.²

Affiliation:

1. School of Science, Xi'an University of Posts and Telecommunications, Xi'an, People's Republic of China

2. School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an, People's Republic of China

Funder

National Science Foundation of China

National Science Basic Research Plan in Shaanxi Province of China

New Star Team of Xi'an University of Posts and Telecommunications

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2021.1975662

Reference25 articles.

1. Local Coordinates Alignment With Global Preservation for Dimensionality Reduction

2. I. Daubechies, R. Devore and M. Fornasier, Iteratively re-weighted least squares minimization: proof of faster than linear rate for sparse recovery, 42nd Annual Conference on Information Sciences and Systems, USA, pp. 26–29, 2008.

3. C. Ding and H. Peng, Minimum redundancy feature selection from microarray gene expression data. Proceedings of 2nd IEEE Computational Systems Bioinformatics Conference. pp. 523–528, August 2003.

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