图 书 馆
降秩隐马尔可夫模型Reduced-rank hidden Markov models |
|
| 课程网址: | http://videolectures.net/aistats2010_boots_rrhm/ |
| 主讲教师: | Byron Boots |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2011-08-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Hsu et al.(2009) recently proposed an efficient, accurate spectral learning algorithm for Hidden Markov Models (HMMs). In this paper we relax their assumptions and prove a tighter finite-sample error bound for the case of Reduced-Rank HMMs, i.e., HMMs with low-rank transition matrices. Since rank-k RR-HMMs are a larger class of models than k-state HMMs while being equally efficient to work with, this relaxation greatly increases the learning algorithm's scope. In addition, we generalize the algorithm and bounds to models where multiple observations are needed to disambiguate state, and to models that emit multivariate real-valued observations. Finally we prove consistency for learning Predictive State Representations, an even larger class of models. Experiments on synthetic data and a toy video, as well as on difficult robot vision data, yield accurate models that compare favorably with alternatives in simulation quality and prediction accuracy. |
| 关 键 词: | 降秩; 马尔可夫模型; 低秩变换矩阵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-09-24：dingaq |
| 阅读次数: | 37 |