图 书 馆
一组用于构造稀疏性的惩罚函数A Family of Penalty Functions for Structured Sparsity |
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| 课程网址: | http://videolectures.net/nips2010_morales_fpf/ |
| 主讲教师: | Jean Morales |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2011-03-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. We present a family of convex penalty functions, which encode this prior knowledge by means of a set of constraints on the absolute values of the regression coefficients. This family subsumes the $\ell_1$ norm and is flexible enough to include different models of sparsity patterns, which are of practical and theoretical importance. We establish some important properties of these functions and discuss some examples where they can be computed explicitly. Moreover, we present a convergent optimization algorithm for solving regularized least squares with these penalty functions. Numerical simulations highlight the benefit of structured sparsity and the advantage offered by our approach over the Lasso and other related methods. |
| 关 键 词: | 稀疏模式; 稀疏线性; 凸罚函数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-06：lxf |
| 阅读次数: | 81 |