Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Cost Sensitive Online Multiple Kernel Classification, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi
Cost Sensitive Online Multiple Kernel Classification, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Learning from data streams has been an important open research problem in the era of big data analytics. This paper investigates supervised machine learning techniques for mining data streams with application to online anomaly detection. Unlike conventional machine learning tasks, machine learning from data streams for online anomaly detection has several challenges: (i) data arriving sequentially and increasing rapidly, (ii) highly class-imbalanced distributions; and (iii) complex anomaly patterns that could evolve dynamically.To tackle these challenges, we propose a novel Cost-Sensitive Online Multiple Kernel Classification (CSOMKC) scheme for comprehensively mining data streams and demonstrate its application to online anomaly detection. Specifically, …
Online Adaptive Passive-Aggressive Methods For Non-Negative Matrix Factorization And Its Applications, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun, Ee-Peng Lim
Online Adaptive Passive-Aggressive Methods For Non-Negative Matrix Factorization And Its Applications, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
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 recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key …