2012 FeatureGroupingandSelectionover

• (Yang et al., 2012) ⇒ Sen Yang, Lei Yuan, Ying-Cheng Lai, Xiaotong Shen, Peter Wonka, and Jieping Ye. (2012). “Feature Grouping and Selection over An Undirected Graph.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339675

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Feature+Grouping+and+Selection+over+An+Undirected+Graph
• http://dl.acm.org/citation.cfm?id=2339530.2339675&preflayout=flat#citedby

Quotes

Author Keywords

• L1 regularization; classification; data mining; feature grouping; feature selection; regression; undirected graph

Abstract

High-dimensional regression / classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression / classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise [math]\displaystyle{ l_1 }[/math]ž norm of connected regression / classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated [math]\displaystyle{ l_1 }[/math] regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 FeatureGroupingandSelectionover	Lei Yuan Jieping Ye Xiaotong Shen Sen Yang Ying-Cheng Lai Peter Wonka			Feature Grouping and Selection over An Undirected Graph				10.1145/2339530.2339675		2012