The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Efficient Spectral Feature Selection with Minimum Redundancy
Open accessAbstract
Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features individually and cannot identify redundant features. Since redundant features can have significant adverse effect on learning performance, it is necessary to address this limitation for spectral feature selection. To this end, we propose a novel spectral feature selection algorithm to handle feature redundancy, adopting an embedded model. The algorithm is derived from a formulation based on a sparse multi-output regression with a L2,1-norm constraint. We conduct theoretical analysis on the properties of its optimal solutions, paving the way for designing an efficient path-following solver. Extensive experiments show that the proposed algorithm can do well in both selecting relevant features and removing redundancy.
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
A tutorial on spectral clustering
von Luxburg, U. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,349
Least Squares Support Vector Machine Classifiers
Suykens, J. A. K., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,362
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,715
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,721
(1998) · Cited by 3,476
Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025
Cited by
Feature Selection: A Data Perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442
Self-citationl 2,1 -norm regularized discriminative feature selection for unsupervised learning
Yang, Y. P., Shen, H. T., Ma, Z., Huang, Z. H., & Zhou, X. (2011) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · doi:10.5591/978-1-57735-516-8/ijcai11-267 · Cited by 549
Unsupervised feature selection by regularized self-representation
Zhu, P., Zuo, W., Zhang, L., Hu, Q., & Shiu, S. C. K. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2014.08.006 · Cited by 323
Multi-Participant Multi-Class Vertical Federated Learning
Feng, S., & Yu, H. (2020) · arXiv · doi:10.48550/arxiv.2001.11154 · Cited by 66
Metadata
- Authors
- Zhao, Z., Wang, L., & Liu, H.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2010-07-03
- Volume
- 24
- Issue
- 1
- Pages
- 673-678
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W1607408214
- Cited by
- 277
- References
- 29
- FWCI
- 12.37
- Type
- conference paper
- Language
- en
- Open access
- diamond
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