Abstract
Canonical correlation analysis(CCA) is a popular technique that works for finding the correlation between two sets of variables. However, CCA faces the problem of small sample size in dealing with high dimensional data. Several approaches have been proposed to overcome this issue, but the resulting transformation matrix fails to extract shared structures among data samples. In this paper, we propose trace norm regularized CCA(SRCCA) that not only tackles the problem of small sample size but also uncover the underlying structures between target classes. Specifically, our formulation characterizes the intrinsic dimensionality of a transformation matrix owing to the appealing property of trace norm. Evaluations over public data sets deliver the effectiveness of our algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
