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内核表示，核密度估计Kernel Representations and Kernel Density Estimation |
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| 课程网址: | http://videolectures.net/sip08_bickel_krakd/ |
| 主讲教师: | Peter J. Bickel |
| 开课单位: | 加州大学 |
| 开课时间: | 2008-12-18 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | There has been a great deal of attention in recent times particularly in machine learning to representation of multivariate data points x by K(x, ·) where K is positive and symmetric and thus induces a reproducing kernel Hilbert space.The idea is then to use the matrix ||K(Xi , Xj )|| as a substitute for the empirical covariance matrix of a sample X1 , . . . , Xn for PCA and other inference.(Jordan and Fukumizu(2006) for instance. Nadler et. al(2006) connected this approach to one based on random walks and diffusion limits and indicated a connection to kernel density estimation.By making at least a formal connection to a multiplication operator on a function space we make further connection and show how clustering results of Beylkin ,Shih and Yu (2008) which apparently differ from Nadler et al. can be explained. |
| 关 键 词: | 机器学习; 多元数据点; 矩阵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-27：zyk |
| 阅读次数: | 77 |