CCEDA: building bridge between subspace projection learning and sparse representation‐based classification. Issue 25 (1st December 2014)

• Record Type: Journal Article
• Authors: Zhu, Qi; Sun, Han; Feng, Qingxiang; Wang, Jinghua
• Is Part Of: Electronics letters. Volume 50:Issue 25(2014)
• Page Start: 1919
• Page End: 1921
• Publication Date: 2014-12-01
• Subjects: face recognition -- image classification -- image representation -- principal component analysis -- learning (artificial intelligence); CCEDA -- subspace projection learning -- sparse representation‐based classification -- face recognition -- SRC -- novel dimensionality reduction method -- class coding error discriminant analysis -- inner‐class coding error -- intra‐class coding error -- primary component analysis -- linear discriminant analysis -- LDA -- PCA; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2014.2816
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Representation‐based classification (SRC) is a face recognition breakthrough of recent years, but the dimensionality reduction for SRC has not been well addressed. The reason why existing dimensionality reduction methods are not effective for SRC is revealed for the first time. Based on analysis of the classification mechanism of SRC, the novel dimensionality reduction method for SRC is proposed, i.e. class coding error discriminant analysis (CCEDA), which simultaneously maximises the inner‐class coding error and minimises the intra‐class coding error. Extensive experiments show that the CCEDA feature achieves a better performance than the other features when using SRC as the classifier.