2015 UnsupervisedFeatureSelectionwit

• (Du & Shen, 2015) ⇒ Liang Du, and Yi-Dong Shen. (2015). “Unsupervised Feature Selection with Adaptive Structure Learning.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783345

Subject Headings: Unsupervised Feature Selection.

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Unsupervised+Feature+Selection+with+Adaptive+Structure+Learning
• http://dl.acm.org/citation.cfm?id=2783258.2783345&preflayout=flat#citedby

Quotes

Author Keywords

• Adaptive structure learning; data mining; unsupervised feature selection

Abstract

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable / inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 UnsupervisedFeatureSelectionwit	Liang Du Yi-Dong Shen			Unsupervised Feature Selection with Adaptive Structure Learning				10.1145/2783258.2783345		2015