图 书 馆
噪声阀值的谱聚类算法Noise Thresholds for Spectral Clustering |
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| 课程网址: | http://videolectures.net/nips2011_balakrishnan_clustering/ |
| 主讲教师: | Sivaraman Balakrishnan |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Although spectral clustering has enjoyed considerable empirical success in machine learning, its theoretical properties are not yet fully developed. We analyze the performance of a spectral algorithm for hierarchical clustering and show that on a class of hierarchically structured similarity matrices, this algorithm can tolerate noise that grows with the number of data points while still perfectly recovering the hierarchical clusters with high probability. We additionally improve upon previous results for k-way spectral clustering to derive conditions under which spectral clustering makes no mistakes. Further, using minimax analysis, we derive tight upper and lower bounds for the clustering problem and compare the performance of spectral clustering to these information theoretic limits. We also present experiments on simulated and real world data illustrating our results. |
| 关 键 词: | 机器学习; 分层集群; 极大极小分析; k谱聚类 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-29：yumf |
| 阅读次数: | 90 |