Label consistent transform learning for pattern classification. (November 2019)

• Record Type: Journal Article
• Authors: Yin, He-Feng; Wu, Xiao-Jun
• Is Part Of: Journal of algorithms & computational technology. Volume 13(2019)
• Publication Date: 2019-11
• Subjects: Transform learning -- dictionary learning -- label consistent -- pattern classification; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748302619881395
• Languages: English
• ISSNs: 1748-3018

Abstract

Transform learning has been successfully applied to various image processing tasks in recent years. Nevertheless, transform learning learns the representation in an unsupervised fashion. To make transform learning suitable for pattern classification, we introduce a label consistency constraint into transform learning and propose a new label consistent transform learning to enhance the classification performance of transform learning. The resulting optimization problem can be solved elegantly by employing the alternative strategy. Experimental results on publicly available databases demonstrate that label consistent transform learning outperforms several dictionary learning approaches and the recently proposed discriminative transform learning. More importantly, label consistent transform learning has the least training time which has the potential in practical applications.