作者
Cemal Okan Sakar, Olcay Kursun
发表日期
2015/12/17
期刊
IEEE Transactions on neural networks and learning systems
卷号
28
期号
1
页码范围
164-176
出版商
IEEE
简介
The canonical correlation analysis (CCA) aims at measuring linear relationships between two sets of variables (views) that can be used for feature extraction in classification problems with multiview data. However, the correlated features extracted by the CCA may not be class discriminative, since CCA does not utilize the class labels in its traditional formulation. Although there is a method called discriminative CCA (DCCA) that aims to increase the discriminative ability of CCA inspired from the linear discriminant analysis (LDA), it has been shown that the extracted features with this method are identical to those by the LDA with respect to an orthogonal transformation. Therefore, DCCA is simply equivalent to applying single-view (regular) LDA to each one of the views separately. Besides, DCCA and the other similar DCCA approaches have generalization problems due to the sample covariance matrices used in …
学术搜索中的文章
CO Sakar, O Kursun - IEEE Transactions on neural networks and learning …, 2015