Conference Proceeding Article
Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem of bounded kernel-based online learning that aims to constrain the number of support vectors by a predefined budget. Although several algorithms have been proposed in literature, they are neither computationally efficient due to their intensive budget maintenance strategy nor effective due to the use of simple Perceptron algorithm. To overcome these limitations, we propose a framework for bounded kernel-based online learning based on an online gradient descent approach. We propose two efficient algorithms of bounded online gradient descent (BOGD) for scalable kernel-based online learning: (i) BOGD by maintaining support vectors using uniform sampling, and (ii) BOGD++ by maintaining support vectors using non-uniform sampling. We present theoretical analysis of regret bound for both algorithms, and found promising empirical performance in terms of both efficacy and efficiency by comparing them to several well-known algorithms for bounded kernel-based online learning on large-scale datasets.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Proceedings of the Twenty-Ninth International Conference on Machine Learning: June 26 - July 1, Edinburgh, Scotland
International Machine Learning Society
City or Country
ZHAO, Peilin; WANG, Jialei; WU, Pengcheng; JIN, Rong; and HOI, Steven C. H..
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning. (2012). Proceedings of the Twenty-Ninth International Conference on Machine Learning: June 26 - July 1, Edinburgh, Scotland. 169-176. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2342
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