TRADING ACCURACY FOR SPARSITY IN OPTIMIZATION PROBLEMS WITH SPARSITY CONSTRAINTS

被引：89

作者：

Shalev-Shwartz, Shai ^{[1
]}

Srebro, Nathan ^{[2
]}

Zhang, Tong ^{[3
]}

机构：

[1] Hebrew Univ Jerusalem, Sch Comp Sci & Engn, Jerusalem, Israel

[2] Toyota Technol Inst, Chicago, IL 60637 USA

[3] Rutgers State Univ, Dept Stat, New Brunswick, NJ 08901 USA

来源：

SIAM JOURNAL ON OPTIMIZATION | 2010年 / 20卷 / 06期

关键词：

sparsity; linear prediction; GREEDY APPROXIMATION; CONVERGENCE; REGRESSION; SELECTION; SYSTEMS; LASSO; WOLFE;

D O I：

10.1137/090759574

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We study the problem of minimizing the expected loss of a linear predictor while constraining its sparsity, i.e., bounding the number of features used by the predictor. While the resulting optimization problem is generally NP-hard, several approximation algorithms are considered. We analyze the performance of these algorithms, focusing on the characterization of the trade-off between accuracy and sparsity of the learned predictor in different scenarios.

引用

页码：2807 / 2832

页数：26

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