L2,1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning

Publication Type:
Conference Proceeding
Citation:
IJCAI International Joint Conference on Artificial Intelligence, 2011, pp. 1589 - 1594
Issue Date:
2011-12-01
Full metadata record
Files in This Item:
Filename Description Size
267.pdfPublished version759.62 kB
Adobe PDF
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 ℓ 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.
Please use this identifier to cite or link to this item: