Unsupervised Discriminant Canonical Correlation Analysis for Feature Fusion

• Published in: 2014 22nd International Conference on Pattern Recognition pp. 1550 - 1555
• Main Authors: Sheng Wang, Xingjian Gu, Jianfeng Lu, Jing-Yu Yang, Ruili Wang, Jian Yang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2014
• Subjects: Algorithm design and analysis; Clustering algorithms; Correlation; Eigenvalues and eigenfunctions; Feature extraction; Linear programming; Training
• ISSN: 1051-4651; 2831-7475
• DOI: 10.1109/ICPR.2014.275

Canonical correlation analysis (CCA) has been widely applied to information fusion. It only considers the correlated information of the paired data, but ignores the correlated information between the samples in the same class. Furthermore, class information is useful for CCA, but there is little class information in the scenarios of real applications. Thus, it is difficult to utilize the correlated information between the samples in the same class. To utilize the correlated information between the samples, we propose a method named Unsupervised Discriminant Canonical Correlation Analysis (UDCCA). In UDCCA, the class membership and mapping are iteratively computed by using the normalized spectral clustering and generalized Eigen value methods alternatively. The experimental results on the MFD dataset and ORL dataset show that UDCCA outperforms traditional CCA and its variants in most situations.