图 书 馆
不相关的多线性主成分分析通过连续的方差最大化Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization |
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| 课程网址: | http://videolectures.net/icml08_lu_ump/ |
| 主讲教师: | Haiping Lu |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2008-08-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applications. This paper extends the classical principal component analysis (PCA) to its multilinear version by proposing a novel dimensionality reduction algorithm for tensorial data, named as uncorrelated multilinear PCA (UMPCA). UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. We evaluate the proposed algorithm on a second-order tensorial problem, face recognition, and the experimental results show its superiority, especially in low-dimensional spaces, through the comparison with three other PCA-based algorithms. |
| 关 键 词: | 主成分分析; 不相关的多线性主成分分析; 降维算法 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2020-05-31：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 128 |