图 书 馆
高维分布的非高斯分布In search of Non-Gaussian Components of a High-Dimensional Distribution |
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| 课程网址: | http://videolectures.net/slsfs05_kawanabe_sngch/ |
| 主讲教师: | Motoaki Kawanabe |
| 开课单位: | ATR脑信息交流研究实验室组 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In high dimensional data analysis, finding non-Gaussian components is an important preprocessing step for efficient information processing. This article proposes a new linear method to identify the non- Gaussian subspace within a very general semi-parametric framework. Our proposed method NGCA (Non-Gaussian Component Analysis) is essentially based on the theoretical fact that, via an arbitrary nonlinear function, a vector which approximately belongs to the low dimensional non-Gaussian subspace can be constructed. Since different nonlinear functions yield different directions, one can obtain an approximate subspace from a set of different nonlinear functions. PCA is then applied to identify the non-Gaussian subspace. A numerical study demonstrates the usefulness of our method. |
| 关 键 词: | 计算机科学; 机器学习; 高斯过程 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-01：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 50 |