Semi-supervised Concept Factorization Algorithm with Local Coordinate Constraint

• Published in: Jisuanji kexue yu tansuo Vol. 15; no. 2; pp. 379 - 388
• Main Author: LI Huirong, ZHANG Lin, ZHAO Pengjun, LI Chao
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.02.2021
• Subjects: concept factorization (cf); local coordinate constraint; non-negative matrix factorization (nmf); semi-supervised learning
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.2004012

Concept factorization (CF) is an effective image representation algorithm, which has been widely used in dimension reduction, feature extraction, data mining and other fields. However, the traditional CF algorithm cannot make use of the limited label information, and fails to guarantee the sparse parts-based representation. Therefore, a novel semi-supervised CF with local coordinate constraint (SLCF) is proposed, which incorporates the local coor-dinates constraint and the limited label information into the CF. Specifically, SLCF enforces the learned coefficients to be sparse by using the local coordinate constraint, and the label constraint matrix can guarantee that the data points sharing the same label are mapped into the same label in the low-dimensional space, so the discriminative ability of different classes is improved. The efficient alternating iterative updating scheme is designed for optimizing SLCF and its convergence is theoretically provided. Numerical experiments on the COIL20, Yale B and MNIST