l2, 1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1589-1593.
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.
@inproceedings{ijcai2011p267,
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 = {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 = {1589--1593},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-267},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-267},
}
TY - CPAPER TI - l2, 1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning AU - Yang, Yi AU - Shen, Heng Tao AU - Ma, Zhigang AU - Huang, Zi AU - Zhou, Xiaofang 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 - 1589 EP - 1593 DO - 10.5591/978-1-57735-516-8/IJCAI11-267 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-267 ER -Copied
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