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Nonparametric Detection of Anomalous Data via Maximum Mean Discrepancy
Date:2015/7/2     Browse:580

Speaker: Yingbin Liang

Time: July 2nd, 9:00-10:00am

Location: Room 220, Building 8, Yueyang Road Campus

Abstract:

This talk will focus on a type of problems, the goal of which is to detect existence of an anomalous object over a network. An anomalous object, if it exists, corresponds to a cluster of nodes in the network that take data samples generated by an anomalous distribution q whereas all other nodes in the network receive samples generated by a distinct distribution p. Such a problem models a variety of applications such as detection of an anomalous intrusion via sensor networks and detection of an anomalous segment in a DNA sequence. All previous studies of this problem have taken parametric models, i.e., distributions p and q are known. Our work studies the nonparametric model, in which distributions can be arbitrary and unknown a priori.

In this talk, I will first introduce the approach that we apply, which is based on mean embedding of distributions into a reproducing kernel Hilbert space (RKHS). In particular, we adopt the quantity of maximum mean discrepancy (MMD) as a metric of distance between mean embeddings of two distributions. I will then present our construction of MMD-based tests for anomalous detection over networks and our analysis of consistency of the proposed tests. I will finally present a number of numerical results to demonstrate our results. Towards the end of the talk, I will discuss some related problems and conclude with a few future directions.

Bio:

Dr. Yingbin Liang received the Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2005. In 2005-2007, she was working as a postdoctoral research associate at Princeton University. In 2008-2009, she was an assistant professor at the University of Hawaii. Since December 2009, she has been on the faculty at Syracuse University, where she is an associate professor. Dr. Liang's research interests include information theory, wireless communications and networks, and machine learning.

Dr. Liang was a Vodafone Fellow at the University of Illinois at Urbana-Champaign during 2003-2005, and received the Vodafone-U.S. Foundation Fellows Initiative Research Merit Award in 2005. She also received the M. E. Van Valkenburg Graduate Research Award from the ECE department, University of Illinois at Urbana-Champaign, in 2005. In 2009, she received the National Science Foundation CAREER Award, and the State of Hawaii Governor Innovation Award. More recently, her paper titled “compound wiretap channels” received the 2014 EURASIP Best Paper Award for the EURASIP Journal on Wireless Communications and Networking.                                                                                                           

SIST-Seminar 15027