图 书 馆
大尺度高斯马尔可夫随机场的方差逼近Variance Approximation in Large-Scale Gaussian Markov Random Fields |
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| 课程网址: | http://videolectures.net/icml09_malioutov_itva/ |
| 主讲教师: | Dmitry Malioutov |
| 开课单位: | 麻省理工学院 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this talk we discuss a framework for computing accurate approximate variances in large scale Gaussian Markov Random Fields. We start by motivating the need to compute variances in GMRFs, and discuss related problems in machine learning. Our approach is based on constructing a certain low-rank aliasing matrix which takes advantage of the Markov graph of the model. We first construct such a matrix for models with short-range correlation, and then describe a wavelet-based construction for models with long-range correlation. The approach is based on fast solution of sparse linear systems, and we describe suitable preconditioners. We also describe how the approach can be used for problems with sparse plus low-rank structure, for example in approximate Kalman filtering with large state spaces. |
| 关 键 词: | 高斯马尔可夫随机场; 精确近似方差; 器学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-23：lxf |
| 阅读次数: | 75 |