2014 SafeandEfficientScreeningforSpa

(Zhao, Liu et al., 2014) ⇒ Zheng Zhao, Jun Liu, and James Cox. (2014). “Safe and Efficient Screening for Sparse Support Vector Machine.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623686

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

Data mining; feature evaluation and selection; feature selection; screening; sparse support vector machine

Abstract

Sparse support vector machine (SVM) is a robust predictive model that can effectively remove noise and preserve signals. Like Lasso, it can efficiently learn a solution path based on a set of predefined parameters and therefore provides strong support for model selection. Sparse SVM has been successfully applied in a variety of data mining applications including text mining, bioinformatics, and image processing. The emergence of big-data analysis poses new challenges for model selection with large-scale data that consist of tens of millions samples and features. In this paper, a novel screening technique is proposed to accelerate model selection for [[l₁-regularized l₂-SVM]] and effectively improve its scalability. This technique can precisely identify inactive features in the optimal solution of a [[l₁-regularized l₂-SVM model]] and remove them before training. The technique makes use of the variational inequality and provides a closed-form solution for screening inactive features in different situations. Every feature that is removed by the screening technique is guaranteed to be inactive in the optimal solution. Therefore, when [[l₁-regularized l₂-SVM]] uses the features selected by the technique, it achieves exactly the same result as when it uses the full feature set. Because the technique can remove a large number of inactive features, it can greatly increase the efficiency of model selection for [[l₁-regularized l₂-SVM]]. Experimental results on five high-dimensional benchmark data sets demonstrate the power of the proposed technique.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 SafeandEfficientScreeningforSpa	Jun Liu Zheng Zhao James Cox			Safe and Efficient Screening for Sparse Support Vector Machine				10.1145/2623330.2623686		2014