Joint Adaptive Graph Learning and Discriminative Analysis for Unsupervised Feature Selection-Reference-Cited by-同舟云学术

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Joint Adaptive Graph Learning and Discriminative Analysis for Unsupervised Feature Selection

Published:2021-05-07 Issue: Volume: Page:
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Zhao Haifeng,Li Qi,Wang Zheng,Nie Feiping

Funder

Science and Technology Planning Project of Guangdong Province

National Natural Science Foundation of China

Key Natural Science Project of Anhui Provincial Education Department

the National Natural Science Foundation of Anhui Province

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/s12559-021-09875-0.pdf

Reference38 articles.

1. Hammer P. Adaptive control processes: A guided tour (r. bellman). Siam Review - SIAM REV 4. 1962. https://doi.org/10.1137/1004050.

2. Mafarja M, Qasem A, Heidari AA, Aljarah I, Faris H, Mirjalili S. Efficient hybrid nature-inspired binary optimizers for feature selection. Cogn Comput. 2020;12(1):150–75.

3. Xu J, Yang G, Yin Y, Man H, He H. Sparse-representation-based classification with structure-preserving dimension reduction. Cogn Comput. 2014;6(3):608–21.

4. Zhou R, Niu L. Feature selection of network data via l2, p regularization. Cogn Comput. 2020;12(6):1217–32.

5. Cai D, Zhang C, He X. Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. 2010;333–342.

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