图 书 馆
稀疏估计与结构化字典Sparse Estimation with Structured Dictionaries |
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| 课程网址: | http://videolectures.net/nips2011_wipf_estimation/ |
| 主讲教师: | David P Wipf |
| 开课单位: | 微软公司 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In the vast majority of recent work on sparse estimation algorithms, performance has been evaluated using ideal or quasi-ideal dictionaries (e.g., random Gaussian or Fourier) characterized by unit $\ell_2$ norm, incoherent columns or features. But in reality, these types of dictionaries represent only a subset of the dictionaries that are actually used in practice (largely restricted to idealized compressive sensing applications). In contrast, herein sparse estimation is considered in the context of structured dictionaries possibly exhibiting high coherence between arbitrary groups of columns and/or rows. Sparse penalized regression models are analyzed with the purpose of finding, to the extent possible, regimes of dictionary invariant performance. In particular, a Type II Bayesian estimator with a dictionary-dependent sparsity penalty is shown to have a number of desirable invariance properties leading to provable advantages over more conventional penalties such as the $\ell_1$ norm, especially in areas where existing theoretical recovery guarantees no longer hold. This can translate into improved performance in applications such as model selection with correlated features, source localization, and compressive sensing with constrained measurement directions. |
| 关 键 词: | 稀疏估计算法; 结构字典; 稀疏惩罚回归模型; 贝叶斯估计 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-11：liush |
| 阅读次数: | 56 |