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Large Scale Online Kernel Learning
Feb 13, 2017Author:
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Title: Large Scale Online Kernel Learning
Authors: Lu, J; Hoi, SCH; Wang, JL; Zhao, PL; Liu, ZY
Author Full Names: Lu, Jing; Hoi, Steven C. H.; Wang, Jialei; Zhao, Peilin; Liu, Zhi-Yong
Language: English
Abstract: In this paper, we present a new framework for large scale online kernel learning, making kernel methods efficient and scalable for large-scale online learning applications. Unlike the regular budget online kernel learning scheme that usually uses some budget maintenance strategies to bound the number of support vectors, our framework explores a completely different approach of kernel functional approximation techniques to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) Nystrom Online Gradient Descent (NOGD) algorithm that applies the Nystrom method to approximate large kernel matrices. We explore these two approaches to tackle three online learning tasks: binary classification, multi-class classification, and regression. The encouraging results of our experiments on large-scale datasets validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget online kernel learning approaches.
ISSN: 1532-4435
Article Number: 47
IDS Number: EH0WS
Unique ID: WOS:000391485400001
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