Publication Type

PhD Dissertation

Publication Date

11-2017

Abstract

One critical deficiency of traditional online kernel learning methods is their increasing
and unbounded number of support vectors (SV’s), making them inefficient and
non-scalable for large-scale applications. Recent studies on budget online learning
have attempted to overcome this shortcoming by bounding the number of SV’s.
Despite being extensively studied, budget algorithms usually suffer from several
drawbacks.
First of all, although existing algorithms attempt to bound the number of SV’s at
each iteration, most of them fail to bound the number of SV’s for the final averaged
classifier, which is commonly used for online-to-batch conversion. To solve this
problem, we propose a novel bounded online kernel learning method, Sparse Passive
Aggressive learning (SPA), which is able to yield a final output kernel-based
hypothesis with a bounded number of support vectors. The idea is to attain the
bounded number of SV’s using an efficient stochastic sampling strategy which samples
an incoming training example as a new SV with a probability proportional to its
loss suffered. Since the SV’s are added wisely and no SV’s are removed during the
learning, the proposed SPA algorithm achieves a bounded final averaged classifier.
We theoretically prove that the proposed SPA algorithm achieves an optimal regret
bound in expectation, and empirically show that the new algorithm outperforms
various bounded kernel-based online learning algorithms.
Secondly, existing budget learning algorithms are either too simple to achieve
satisfactory approximation accuracy, or too computationally intensive to scale for
large datasets. To overcome this limitation and make online kernel learning efficient
and scalable, we explore two functional approximation based online kernel
machine learning algorithms, Fourier Online Gradient Descent (FOGD) and Nystr¨om Online Gradient Descent (NOGD). The main idea is to adopt the two methods
to approximate the kernel model with a linear classifier, so that the efficiency is
highly improved. 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
Thirdly, we also extend the proposed algorithms to solve the online multiple kernel
learning (OMKL) problem, in which the goal is to significantly reduce the learning
complexity of Online Multiple Kernel Classification (OMKC) while achieving
satisfactory accuracy as compared with existing unbounded OMKC algorithms. We
theoretically prove that the proposed algorithms achieve an optimal regret bound,
and empirically show that the new algorithms outperform various bounded kernelbased
online learning algorithms.
In conclusion, this work presents several novel solutions for scaling up online kernel
learning algorithms for large scale applications. To facilitate other researchers,
we also provide an open source software tool-box that includes the implementation
of all proposed algorithms in this paper, as well as many state-of-the-art online kernel
learning algorithms.

Keywords

machine learning, online learning, kernel learning, classification, kernel approximation, scalable learning

Degree Awarded

PhD in Information Systems

Discipline

Computer and Systems Architecture | Online and Distance Education

Supervisor(s)

HOI, Chu Hong

Copyright Owner and License

Singapore Management University

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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