图 书 馆
基于典型相关分析的多视图聚类Multi-View Clustering via Canonical Correlation Analysis |
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| 课程网址: | http://videolectures.net/icml09_chaudhuri_mvc/ |
| 主讲教师: | Kamalika Chaudhuri |
| 开课单位: | 加州大学圣地亚哥分校 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years address this problem by projecting the data into a lower dimensional subspace, e.g. via Principal Components Analysis (PCA) or random projections, before clustering. Here, we consider constructing such projections using multiple views of the data, via Canonical Correlation Analysis (CCA). Under the assumption that the views are uncorrelated given the cluster label, we show that the separation conditions required for the algorithm to be successful are significantly weaker than prior results in the literature. We provide results for mixtures of Gaussians and mixtures of log concave distributions. We also provide empirical support from audio-visual speaker clustering (where we desire the clusters to correspond to speaker ID) and from hierarchical Wikipedia document clustering (where one view is the words in the document and the other is the link structure). |
| 关 键 词: | 高维聚类数据; 有效聚类算法; 高斯混合物 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-21：lxf |
| 阅读次数: | 245 |