By Topic

Sparse 2-D Canonical Correlation Analysis via Low Rank Matrix Approximation for Feature Extraction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jingjie Yan ; Res. Center for Learning Sci., Southeast Univ., Nanjing, China ; Zheng, Wenming ; Zhou, Xiaoyan ; Zhijian Zhao

Although 2-D canonical correlation analysis (2DCCA) has been proposed to reduce the computational complexity while reserving local data structure of image, the learned canonical variables of 2DCCA are the linear combination of all the original variables, which makes it hard to interpret the solutions and might have less generality. In this paper, we propose a sparse 2-D canonical correlation analysis (S2DCCA) to solve the drawbacks of the 2DCCA method and apply it to image feature extraction. The basic idea of S2DCCA is to impose two lasso penalties on the objective function of 2DCCA to obtain two sets of sparse projection directions via low rank matrix approximation. We conduct extensive experiments on both FERET and AR databases to evaluate the performance of the proposed method.

Published in:

Signal Processing Letters, IEEE  (Volume:19 ,  Issue: 1 )