Non-linear Feature Selection Based on Convolution Neural Networks with Sparse Regularization-Reference-Cited by-同舟云学术

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Non-linear Feature Selection Based on Convolution Neural Networks with Sparse Regularization

Published:2023-12-01 Issue:2 Volume:16 Page:654-670
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Wu Wen-Bin^ORCID,Chen Si-Bao,Ding Chris,Luo Bin

Funder

National Natural Science Foundation of China

Key Project of International (Regional) Cooperation and Exchanges

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-023-10230-8.pdf

Reference26 articles.

1. Zhang S, Lang Z-Q. Orthogonal least squares based fast feature selection for linear classification. Patt Recog. 2022;3(123):108419.

2. Shang R, Zhang X, Feng J, et al. Sparse and low-dimensional representation with maximum entropy adaptive graph for feature selection. Neurocomputing. 2022;7(485):57–73.

3. Hallajian B, Motameni H, Akbari E. Ensemble feature selection using distance-based supervised and unsupervised methods in binary classification. Exp Syst Appl. 2022;15(200):116794.

4. Li M, Huan J, Yang J. Automatic feature extraction and fusion recognition of motor imagery EEG using multilevel multiscale CNN. Med Biol Eng Comput. 2021;59:2037–50.

5. Chen S, Ding CHQ, Zhou Z, Luo B. Feature selection based on correlation deflation. Neural Comput Appl. 2019;10(31):6383–92.

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