图 书 馆
辅助变信息最大化降维Auxillary Variational Information Maximization for Dimensionality Reduction |
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| 课程网址: | http://videolectures.net/slsfs05_barber_avimd/ |
| 主讲教师: | David Barber |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Mutual Information (MI) is a long studied measure of in- formation content, and many attempts to apply it to feature extraction and stochastic coding have been made. However, in general MI is com- putationally intractable to compute, and most previous studies redefine the criterion in forms of approximations. Recently we described proper- ties of a simple lower bound on MI [2], and discussed its links to some of the popular dimensionality reduction techniques. Here we introduce a richer family of the auxiliary variational bounds on MI, which gener- alize our previous approximations. Our specific focus then is on apply- ing the bound to extracting informative lower-dimensional projections in the presence of irreducible Gaussian noise. We show that our method produces significantly tighter bounds on MI compared with the as-if Gaussian approximation [7]. We also show that learning projections to multinomial auxiliary spaces may facilitate reconstructions of the sources from noisy lower-dimensional representations. |
| 关 键 词: | 互信息; 降维技术; 具体聚焦 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-25：liush |
| 阅读次数: | 47 |