The Hong Kong University of Science and Technology
Department of Computer Science


PhD Thesis Defence


"Semi-Supervised Distance Metric Learning"

By

Miss Hong Chang


Abstract

Many machine learning and pattern recognition algorithms rely on a
distance metric. Instead of choosing a metric manually, a more promising
approach is to learn the metric from data automatically. Besides some
early work on metric learning for classification, more and more efforts
have been devoted in recent years to learning a distance metric under the
semi-supervised learning setting. Semi-supervised learning is a learning
paradigm between the supervised and unsupervised learning extremes.
Algorithms of this class usually solve the classification or clustering
problems with the aid of additional background knowledge. While there has
been a whole set of interesting ideas on how to learn from data with
supervisory information, we focus our study on semi-supervised learning in
the metric learning context.

In this thesis, we propose a series of novel methods for semi-supervised
distance metric learning with additional information in the form of
pairwise similarity and dissimilarity constraints. More specifically,
metric learning in nonparametric and parametric forms, kernel-based metric
learning, and metric learning based on manifold structure will be
presented in turn. We apply our methods to some real-world applications,
such as content-based image retrieval and color image segmentation.
Experimental results show that our proposed methods outperform previous
metric learning methods.


Date:			Tuesday, 17 January 2006

Time:			10:00a.m.-12:00noon

Venue:			Room 4480
			Lifts 25-26

Chairman:		Prof. Angelina Yee (HUMA)

Committee Members:	Prof. Dit Yan Yeung (Supervisor)
			Prof. James Kwok
			Prof. Nevin Zhang
			Prof. Michael Wong (PHYS)
			Prof. Anil Jain (Comp. Sci. & Engg.,
					 Michigan State Univ.)


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