图 书 馆
稀疏逆高斯过程回归及其在气候网络发现中的应用Sparse Inverse Gaussian Process Regression with Application to Climate Network Discovery |
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| 课程网址: | http://videolectures.net/cidu2011_das_regression/ |
| 主讲教师: | Kamalika Das |
| 开课单位: | 美国宇航局 |
| 开课时间: | 2012-06-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to large data sets since prediction based on the learned model requires inversion of an order n kernel matrix. Approximate solutions for sparse Gaussian Processes have been proposed for sparse problems. However, in almost all cases, these solution techniques are agnostic to the input domain and do not preserve the similarity structure in the data. As a result, although these solutions sometimes provide excellent accuracy, the models do not have interpretability. Such interpretable sparsity patterns are very important for many applications. We propose a new technique for sparse Gaussian Process regression that allows us to compute a parsimonious model while preserving the interpretability of the sparsity structure in the data. We discuss how the inverse kernel matrix used in Gaussian Process prediction gives valuable domain information and then adapt the inverse covariance estimation from Gaussian graphical models to estimate the Gaussian kernel. We solve the optimization problem using the alternating direction method of multipliers that is amenable to parallel computation. We demonstrate the performance of our method in terms of accuracy, scalability and interpretability on a climate data set. |
| 关 键 词: | 大规模数据集; 高斯过程回归; 稀疏问题; 气候数据集; 计算机科学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-09-17：chenxin |
| 阅读次数: | 37 |