Online adaptive passive-aggressive methods for non-negative matrix factorization and its applications

• Main Authors: LIU, Chenghao, HOI, Steven C. H., ZHAO, Peilin, SUN, Jianling, LIM, Ee-Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Non-Negative Matrix Factorization; Online Learning; Adaptive; Regularization; Learning with Expert Advice; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3450; https://ink.library.smu.edu.sg/context/sis_research/article/4451/viewcontent/Online_Adaptive_Passive_Aggressive_Methods.pdf
• Institution: Singapore Management University

This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a...