A novel general kernel-based non-negative matrix factorisation approach for face recognition. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Chen, Wen-Sheng; Ge, Xiya; Pan, Binbin
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 785
• Page End: 810
• Publication Date: 2022-12-31
• Subjects: Non-negative matrix factorisation -- Kernel function -- face recognition; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2021.1988904
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : Kernel-based non-negative matrix factorisation (KNMF) is a promising nonlinear approach for image data representation using non-negative features. However, most of the KNMF algorithms are developed via a specific kernel function and thus fail to adopt other kinds of kernels. Also, they have to learn pre-image inaccurately that may influence the reliability of the method. To address these problems of KNMF, this paper proposes a novel general kernel-based non-negative matrix factorisation (GKBNNMF) method. It not only avoids pre-image learning but also is suitable for any kernel functions as well. We assume that the mapped basis images fall within the cone spanned by the mapped training data, allowing us to use arbitrary kernel function in the algorithm. The symmetric NMF strategy is exploited on kernel matrix to establish our general kernel NMF model. The proposed algorithm is proven to be convergent. The facial image datasets are selected to evaluate the performance of our method. Compared with some state-of-the-art approaches, the experimental results demonstrate that our proposed is both effective and robust.