基于L1的松弛用于稀疏度恢复和高维体系中的图形模型选择][L1-based relaxations for sparsity recovery and graphical model selection in the high-dimensional regime]

基于L1的松弛用于稀疏度恢复和高维体系中的图形模型选择 L1-based relaxations for sparsity recovery and graphical model selection in the high-dimensional regime


课程网址:	http://videolectures.net/mlss06tw_wainwright_brsrg/
主讲教师:	Martin J. Wainwright
开课单位:	加州大学伯克利分校
开课时间:	2007-02-25
课程语种:	英语
中文简介:	估计嵌入在噪声中的稀疏信号的问题出现在各种环境中，包括信号去噪和近似，以及图形模型选择。这些问题的自然优化理论公式涉及“范数”约束（即，对非零系数的数量的惩罚），这通常导致NP难问题。一种自然的方法是考虑使用-norm作为计算上易处理的替代，正如在信号处理和统计中所追求的那样。
课程简介:	The problem of estimating a sparse signal embedded in noise arises in various contexts, including signal denoising and approximation, as well as graphical model selection. The natural optimization-theoretic formulation of such problems involves "norm" constraints (i.e., penalties on the number of non-zero coefficients), which leads to NP-hard problems in general. A natural approach is to consider the use of the -norm as a computationally tractable surrogate, as has been pursued in both signal processing and statistics.
关键词:	稀疏信号; 图形模型选择; 自然优化理论公式
课程来源:	视频讲座网
最后编审:	2019-08-09：cjy
阅读次数:	78

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