TY - GEN
T1 - Unsupervised discriminant canonical correlation analysis for feature fusion
AU - Wang, Sheng
AU - Gu, Xingjian
AU - Lu, Jianfeng
AU - Yang, Jing Yu
AU - Wang, Ruili
AU - Yang, Jian
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/4
Y1 - 2014/12/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84919905118&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2014.275
DO - 10.1109/ICPR.2014.275
M3 - Conference contribution
AN - SCOPUS:84919905118
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1550
EP - 1555
BT - Proceedings - International Conference on Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Pattern Recognition, ICPR 2014
Y2 - 24 August 2014 through 28 August 2014
ER -