A Local Method for Canonical Correlation Analysis

被引：2
作者：
Ye, Tengju [1 ]
Xie, Zhipeng [1 ]
Li, Ang [1 ]
机构：
[1] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China
来源：
NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2015 | 2015年 / 9362卷
关键词：
Local linearity; Multivariate analysis; Cross-modal multimedia retrieval; REDUCTION;
D O I：
10.1007/978-3-319-25207-0_39
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Canonical Correlation Analysis (CCA) is a standard statistical technique for finding linear projections of two arbitrary vectors that are maximally correlated. In complex situations, the linearity of CCA is not applicable. In this paper, we propose a novel local method for CCA to handle the non-linear situations. We aim to find a series of local linear projections instead of a single globe one. We evaluate the performance of our method and CCA on two real-world datasets. Our experiments show that local method outperforms original CCA in several realistic cross-modal multimedia retrieval tasks.
引用
收藏
页码：428 / 435
页数：8
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