Dual semi-supervised convex nonnegative matrix factorization for data representation

• المؤلفون الرئيسيون: Peng, Siyuan, Yang, Zhijing, Ling, Bingo Wing-Kuen, Chen, Badong, Lin, Zhiping
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Electrical and electronic engineering; Semi-Supervised Learning; Data Representation
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/161773
• المؤسسة: Nanyang Technological University

Semi-supervised nonnegative matrix factorization (NMF) has received considerable attention in machine learning and data mining. A new semi-supervised NMF method, called dual semi-supervised convex nonnegative matrix factorization (DCNMF), is proposed in this paper for fully using the limited label i...