L2, 1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning
Abstract
Compared with supervised learning for feature selection, it is much more difficult to select the discriminative features in unsupervised learning due to the lack of label information. Traditional unsupervised feature selection algorithms usually select the features which best preserve the data distribution, e.g., manifold structure, of the whole feature set. Under the assumption that the class label of input data can be predicted by a linear classifier, we incorporate discriminative analysis and `2;1-norm minimization into a joint framework for unsupervised feature selection. Different from existing unsupervised feature selection algorithms, our algorithm selects the most discriminative feature subset from the whole feature set in batch mode. Extensive experiment on different data types demonstrates the effectiveness of our algorithm.
Cite
Text
Yang et al. "L2, 1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-267Markdown
[Yang et al. "L2, 1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/yang2011ijcai-l/) doi:10.5591/978-1-57735-516-8/IJCAI11-267BibTeX
@inproceedings{yang2011ijcai-l,
title = {{L2, 1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning}},
author = {Yang, Yi and Shen, Heng Tao and Ma, Zhigang and Huang, Zi and Zhou, Xiaofang},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {1589-1594},
doi = {10.5591/978-1-57735-516-8/IJCAI11-267},
url = {https://mlanthology.org/ijcai/2011/yang2011ijcai-l/}
}