PhD Thesis Proposal Defence


"Some Research Problems in Metric Learning
and Manifold Learning"

By Miss Hong Chang


Abstract:

In the past few years, metric learning, semi-supervised learning, and
manifold learning methods have aroused a great deal of interest in the
machine learning community.  Many machine learning and pattern recognition
algorithms rely on a distance metric.  Instead of choosing the metric
manually, a promising approach is to learn the metric from data
automatically. Besides some early work on metric learning for classcation,
more and more efforts have been devoted in recent years to learning a
distance metric for the semi-supervised learning setting. Semisupervised
learning is a learning paradigm between the supervised and unsupervised
learning extremes. Algorithms of this class usually solve the classcation
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 semisupervised
learning in the metric learning context.

Manifold learning mainly aims to automatically discover the
low-dimensional nonlinear manifold in a high-dimensional data space and
then embed the data points into a low-dimensional embedding space. Besides
manifold learning for data representation, some researchers have explored
the possibility of using manifold structure for supervised and
unsupervised learning tasks. We have explored some possibilities for the
solutions of several research problems in manifold learning under the
unsupervised learning setting.

To summarize, in this proposal, we will focus on several research problems
in metric learning under the semi-supervised learning setting and manifold
learning under the unsupervised learning setting. On one hand, we will
report some primary work that has been done. On the other hand, we will
propose some possible future research directions.


Date:     		Tuesday, 9 November 2004

Time:                   2:00p.m.-4:00p.m.

Venue:                  Room 2404
			lifts 17-18

Committee Members:      Prof. Dit-Yan Yeung (Supervisor)
			Prof. James Kwok (Chairperson)
			Prof. Brian Mak
			Prof. Nevin Zhang


**** ALL are Welcome ****