2009 LargeScaleSparseLogisticRegress

• (Liu et al., 2009) ⇒ Jun Liu, Jianhui Chen, and Jieping Ye. (2009). “Large-scale Sparse Logistic Regression.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557082

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Management: Database Applications - Data Mining.
• General Terms: Algorithms

Cited By

• http://scholar.google.com/scholar?q=%22Large-scale+sparse+logistic+regression%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557082&preflayout=flat#citedby

Quotes

Author Keywords

Logistic Regression, Sparse Learning, L1-ball Constraint, Nesterov’s method, Adaptive Line Search

Abstract

Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Sparse logistic regression embeds feature selection in the classification framework using the L1-norm regularization, and is attractive in many applications involving high-dimensional data. In this paper, we propose Lassplore for solving large-scale sparse logistic regression. Specifically, we formulate the problem as the L1-ball constrained smooth convex optimization, and propose to solve the problem using the Nesterov's method, an optimal first-order black-box method for smooth convex optimization. One of the critical issues in the use of the Nesterov's method is the estimation of the step size at each of the optimization iterations. Previous approaches either applies the constant step size which assumes that the Lipschitz gradient is known in advance, or requires a sequence of decreasing step size which leads to slow convergence in practice. In this paper, we propose an adaptive line search scheme which allows to tune the step size adaptively and meanwhile guarantees the optimal convergence rate. Empirical comparisons with several state-of-the-art algorithms demonstrate the efficiency of the proposed Lassplore algorithm for large-scale problems.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 LargeScaleSparseLogisticRegress	Jieping Ye Jun Liu Jianhui Chen			Large-scale Sparse Logistic Regression		KDD-2009 Proceedings		10.1145/1557019.1557082		2009