图 书 馆
核特征空间中的匹配追踪理论Theory of Matching Pursuit in Kernel Defined Feature Spaces |
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| 课程网址: | http://videolectures.net/wehys08_shawe_taylor_tmp/ |
| 主讲教师: | John Shawe-Taylor |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2008-12-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound is tighter than the KPCA bound of Shawe-Taylor et al and highly predictive of the size of the subspace needed to capture most of the variance in the data. We analyse a second matching pursuit algorithm called kernel matching pursuit (KMP) which does not correspond to a sample compression scheme. However, we give a novel bound that views the choice of subspace of the KMP algorithm as a compression scheme and hence provide a VC bound to upper bound its future loss. Finally we describe how the same bound can be applied to other matching pursuit related algorithms. |
| 关 键 词: | 计算机科学; 机器学习; 核方法; 核主成分分析 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-02-04：nkq |
| 阅读次数: | 48 |