The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
Abstract
This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.
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Ang, J. C., Mirzal, A., Haron, H., & Hamed, H. N. A. (2016) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2478454 · Cited by 641
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Zhang, R., Nie, F., Li, X., & Wei, X. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.019 · Cited by 313
Self-citationEnsemble manifold regularized sparse low-rank approximation for multiview feature embedding
Zhang, L., Zhang, Q., Zhang, L., Tao, D., Huang, X., & Du, B. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2014.12.016 · Cited by 278
Global and Local Structure Preservation for Feature Selection
Liu, X., Wang, L., Zhang, J., Yin, J., & Liu, H. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2287275 · Cited by 256
Feature Selection Boosted by Unselected Features
Zheng, W., Chen, S., Fu, Z., Zhu, F., Yan, H., & Yang, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3058172 · Cited by 51
Metadata
- Authors
- Xiang, S., Nie, F., Meng, G., Pan, C., & Zhang, C.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2012-11
- Volume
- 23
- Issue
- 11
- Pages
- 1738-1754
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2043080228
- Cited by
- 473
- References
- 60
- FWCI
- 13.11
- Type
- journal article
- Language
- en
- Open access
- closed
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