图 书 馆
基于典型相关分析的多视图聚类Multiview Clustering via Canonical Correlation Analysis |
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| 课程网址: | http://videolectures.net/lms08_livescu_mvc/ |
| 主讲教师: | Karen Livescu |
| 开课单位: | 芝加哥丰田技术学院 |
| 开课时间: | 2008-12-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Clustering algorithms such as k-means perform poorly when the data is highdimensional. 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. Such techniques typically require stringent requirements on the separation between the cluster means. Here we present ongoing work on projection-based clustering that addresses this using multiple views of the data. We use canonical correlation analysis (CCA) to project the data in each view to a lower-dimensional subspace. Under the assumption that the correlated dimensions capture the information about the cluster identities, the separation conditions required for the algorithm to be successful are significantly weaker than those of prior results in the literature. We describe experiments on two domains, (a) speech audio and images of the speakers’ faces, and (b) text and links in Wikipedia articles. We discuss several issues that arise when clustering in these domains, in particular the existence of multiple possible “cluster variables” and of a hierarchical cluster structure. |
| 关 键 词: | 聚类算法; 主成分分析; 数据视图 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：cjy |
| 阅读次数: | 83 |