Discovering support and affiliated features from very high dimensions

Publication Type:
Conference Proceeding
Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012, 2 pp. 1455 - 1462
Issue Date:
Filename Description Size
Thumbnail2013004285OK.pdf637.23 kB
Adobe PDF
Full metadata record
In this paper, a novel learning paradigm is presented to automatically identify groups of informative and correlated features from very high dimensions. Specifically, we explicitly incorporate correlation measures as constraints and then propose an efficient embedded feature selection method using recently developed cutting plane strategy. The benefits of the proposed algorithm are two-folds. First, it can identify the optimal discriminative and uncorrected feature subset to the output labels, denoted here as Support Features, which brings about significant improvements in prediction performance over other state of the art feature selection methods considered in the paper. Second, during the learning process, the underlying group structures of correlated features associated with each support feature, denoted as Affiliated Features, can also be discovered without any additional cost. These affiliated features serve to improve the interpretations on the learning tasks. Extensive empirical studies on both synthetic and very high dimensional real-world datasets verify the validity and efficiency of the proposed method. Copyright 2012 by the author(s)/owner(s).
Please use this identifier to cite or link to this item: