图 书 馆
基于示例的集群的灵活先验Flexible Priors for Exemplar-based Clustering |
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| 课程网址: | http://videolectures.net/uai08_tarlow_fp/ |
| 主讲教师: | Daniel Tarlow |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2008-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Exemplar-based clustering methods have been shown to produce state-of-the-art results on a number of synthetic and real-world clustering problems. They are appealing because they offer computational benefits over latent-mean models and can handle arbitrary pairwise similarity measures between data points. However, when trying to recover underlying structure in clustering problems, tailored similarity measures are often not enough; we also desire control over the distribution of cluster sizes. Priors such as Dirichlet process priors allow the number of clusters to be unspecified while expressing priors over data partitions. To our knowledge, they have not been applied to exemplar-based models. We show how to incorporate priors, including Dirichlet process priors, into the recently introduced affinity propagation algorithm. We develop an efficient max product belief propagation algorithm for our new model and demonstrate experimentally how the expanded range of clustering priors allows us to better recover true clusterings in situations where we have some information about the generating process. |
| 关 键 词: | 聚类方法; 潜在均值模型; 群集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-04：cjy |
| 阅读次数: | 32 |