Work

Label consistent transform learning for pattern classification

Year: 2019

Type: article

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.

Source: Journal of Algorithms & Computational Technology

DOI: 10.1177/1748302619881395

Authors: He-Feng Yin, Xiao-Jun Wu

Institution: Jiangnan University

Language: English

Topic: Face and Expression Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $900

Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China