Title

:

Robust Models and Methods in Unsupervised Learning over Uncertain Data

Speaker

:

Zhenjie Zhang

Date

:

April 14th, 2008 (Monday)

Time

:

4:30 p.m. - 5:30 p.m.

Venue

:

Room 513

William M.W. Mong Engineering Building

(Engineering Building Complex Phase 2)

CUHK

Uncertain data is now ubiquitous in many database systems and
applications, such as scientific database, sensor network, moving
objects and data stream, due to inaccurate measurement or infrequent
data update. In this talk, I will present our new studies on
unsupervised learning over uncertain data sets. In our study, every
uncertain object is modeled as a sphere in the corresponding space, in
which the exact position is bounded without any underlying
distribution assumption. Based on the definition of uncertainty,
different computation models are proposed for unsupervised learning
tasks, including Zero Uncertain Model, Static Uncertain Model,
Dissolvable Uncertain Model and Reversed Uncertain Model. Each of the
models can be applied to different environments with different
requirements. I will further present some preliminary solutions to the
models with some of the popular learning algorithms, such as k-means
algorithm, EM algorithm.

Zhang Zhenjie is currently a PhD candidate in the School of Computing,
National University of Singapore working with Dr Anthony K.H. Tung. He
received his B.Sc. from the Department of Computer Science and
Engineering, Fudan University, China. His research interests include
general skyline query, unsupervised learning, and game theoretical
analysis over large data. Zhenjie presently has 9 research papers to
his name including papers in major venues such as SIGMOD and TKDE. He
was a recipient of the prestigious NUS President Fellowship in 2007
and is a student member of both the ACM and IEEE.

Host

:

Prof. Yu Xu, Jeffrey

Tel

:

(852) 2609-8309

Email

:

yu@se.cuhk.edu.hk

Enquiries

:

Prof. Nan Chen or Prof. Sean X. Zhou

:

Department of Systems Engineering and Engineering Management

CUHK

Website

:

http://www.se.cuhk.edu.hk/~seg5810

Email

:

seg5810@se.cuhk.edu.hk