图 书 馆
部分隶属度的统计模型Statistical Models for Partial Membership |
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| 课程网址: | http://videolectures.net/icml08_heller_smpm/ |
| 主讲教师: | Heller Katherine A |
| 开课单位: | 杜克大学 |
| 开课时间: | 2008-08-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs to one and only one mixture component, or cluster, a partial membership model allows data points to have fractional membership in multiple clusters. Algorithms which assign data points partial memberships to clusters can be useful for tasks such as clustering genes based on microarray data and global positioning and orbit determination. Our Bayesian Partial Membership Model (BPM) uses exponential family distributions to model each cluster, and a product of these distibtutions, with weighted parameters, to model each datapoint. Here the weights correspond to the degree to which the datapoint belongs to each cluster. All parameters in the BPM are continuous, so we can use Hybrid Monte Carlo to perform inference and learning. We discuss relationships between the BPM and Latent Dirichlet Allocation, Mixed Membership models, Exponential Family PCA, and fuzzy clustering. Lastly, we show some experimental results and discuss nonparametric extensions to our model. |
| 关 键 词: | 建模数据点; 贝叶斯框架; 聚类基因 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-30：lxf |
| 阅读次数: | 36 |