图 书 馆
马尔可夫网络的初等和对偶稀疏性On Primal and Dual Sparsity of Markov Networks |
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| 课程网址: | http://videolectures.net/icml09_zhu_pdsm/ |
| 主讲教师: | Jun Zhu |
| 开课单位: | 清华大学 |
| 开课时间: | 2009-09-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Sparsity is a desirable property in high dimensional learning. The $\ell_1$-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity; but achieving both in a single structured prediction model remains difficult. This paper presents an $\ell_1$-norm max-margin Markov network ($\ell_1$-M$^3$N), which enjoys both primal and dual sparsity, and analyzes its connections to the Laplace max-margin Markov network (LapM$^3$N), which inherits the dual sparsity of max-margin models but is pseudo-primal sparse. We show that $\ell_1$-M$^3$N is an extreme case of LapM$^3$N when the regularization constant is infinity. We also show an equivalence between $\ell_1$-M$^3$N and an adaptive M$^3$N, from which we develop a robust EM-style algorithm for $\ell_1$-M$^3$N. We demonstrate the advantages of the simultaneously (pseudo-) primal and dual sparse models over the ones which enjoy either primal or dual sparsity on both synthetic and real data sets. |
| 关 键 词: | 稀疏性; 高维学习; 范数正则化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：cwx |
| 阅读次数: | 68 |