Joint Feature Selection and Subspace Learning

Gu, Quanquan; Li, Zhenhui; Han, Jiawei

doi:10.5591/978-1-57735-516-8/IJCAI11-219

Joint Feature Selection and Subspace Learning

Quanquan Gu, Zhenhui Li, Jiawei Han

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1294-1298.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-219

Abstract

Dimensionality reduction is a very important topic in machine learning. It can be generally classified into two categories: feature selection and subspace learning. In the past decades, many methods have been proposed for dimensionality reduction. However, most of these works study feature selection and subspace learning independently. In this paper, we present a framework for joint feature selection and subspace learning. We reformulate the subspace learning problem and use L2,1-norm on the projection matrix to achieve row-sparsity, which leads to selecting relevant features and learning transformation simultaneously. We discuss two situations of the proposed framework, and present their optimization algorithms. Experiments on benchmark face recognition data sets illustrate that the proposed framework outperforms the state of the art methods overwhelmingly.

Quanquan Gu, Zhenhui Li, and Jiawei Han. 2011. Joint Feature Selection and Subspace Learning. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1294–1298. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-219

@inproceedings{ijcai2011p219,
  title     = {Joint Feature Selection and Subspace Learning},
  author    = {Gu, Quanquan and Li, Zhenhui and Han, Jiawei},
  booktitle = {Proceedings of the Twenty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-11}},
  publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
  editor    = {Toby Walsh},
  pages     = {1294--1298},
  year      = {2011},
  month     = {7},
  doi       = {10.5591/978-1-57735-516-8/IJCAI11-219},
  url       = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-219},
}

TY  - CPAPER
TI  - Joint Feature Selection and Subspace Learning
AU  - Gu, Quanquan
AU  - Li, Zhenhui
AU  - Han, Jiawei
T2  - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11
PB  - AAAI Press/International Joint Conferences on Artificial Intelligence
PY  - 2011
SP  - 1294
EP  - 1298
DO  - 10.5591/978-1-57735-516-8/IJCAI11-219
UR  - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-219
ER  -