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稀疏主成分分析的密集消息传递Dense message passing for sparse principal component analysis |
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| 课程网址: | http://videolectures.net/aistats2010_sharp_dmpfs/ |
| 主讲教师: | Kevin Sharp |
| 开课单位: | 曼彻斯特大学 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of this type. MCMC procedures are too slow to be practical in a very high-dimensional setting and standard mean-field variational Bayes algorithms are ineffective. We adopt a dense message passing algorithm similar to algorithms developed in the statistical physics community and previously applied to inference problems in coding and sparse classification. The algorithm achieves near-optimal performance on synthetic data for which a statistical mechanics theory of optimal learning can be derived. We also study two gene expression datasets used in previous studies of sparse PCA. We find our method performs better than one published algorithm and comparably to a second. |
| 关 键 词: | 稀疏主成分; 密集消息传递 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-30：cwx |
| 阅读次数: | 27 |