图 书 馆
典型相关分析的最小二乘公式A Least Squares Formulation for Canonical Correlation Analysis |
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| 课程网址: | http://videolectures.net/icml08_ji_alsf/ |
| 主讲教师: | Shuiwang Ji |
| 开课单位: | 华盛顿州立大学 |
| 开课时间: | 2008-08-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multi-dimensional variables. It projects both sets of variables into a lower-dimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction, in which one of the multi-dimensional variables is derived from the class label. It has been shown that CCA can be formulated as a least squares problem in the binary-class case. However, their relationship in the more general setting remains unclear. In this paper, we show that, under a mild condition which tends to hold for high-dimensional data, CCA in multi-label classifications can be formulated as a least squares problem. Based on this equivalence relationship, we propose several CCA extensions including sparse CCA using 1-norm regularization. Experiments on multi-label data sets confirm the established equivalence relationship. Results also demonstrate the effectiveness of the proposed CCA extensions |
| 关 键 词: | 典型相关分析; 多维变量; 最小二乘 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-19：lxf |
| 阅读次数: | 90 |