Cross-modal correlation feature extraction with orthogonality redundancy reduce and discriminant structure constraint
by Qianjin Zhao; Xinrui Ping; Shuzhi Su
International Journal of Computational Science and Engineering (IJCSE), Vol. 26, No. 1, 2023

Abstract: Canonical correlation analysis (CCA) is a classic feature extraction method that is widely used in the field of pattern recognition. Its goal is to learn correlation projection directions to maximise the correlation between the two sets of variables, but it does not take into consideration the class label information among samples and the within-modal redundancy information from the correlation projection directions. To this end, a novel method of class label embedding orthogonal correlation feature extraction method is proposed in this paper. The label-guide discriminant structure information is deeply embedded in correlative analytical theories for improving discrimination of correlation features, and within-modal orthogonality constraints are added to further reduce the projection redundancy of correlation features. Several validation experiments on the GT, Umist and YALE datasets are designed, which demonstrates that DOCCA method is superior to other feature extraction methods and achieves state-of-the-art performance. This method provides a new solution to pattern recognition.

Online publication date: Thu, 23-Feb-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com