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
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
收藏
页码:2807 / 2832
页数:26
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