Robust sparse nonnegative matrix factorization based on maximum correntropy criterion

• المؤلفون الرئيسيون: Peng, Siyuan, Ser, Wee, Lin, Zhiping, Chen, Badong
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2020
• الموضوعات: Engineering::Electrical and electronic engineering; Matrix Decomposition; Clustering Algorithms
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/140395

Nonnegative matrix factorization (NMF) is a significant matrix decomposition technique for learning parts-based, linear representation of nonnegative data, which has been widely used in a broad range of practical applications such as document clustering, image clustering, face recognition and blind...