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Home | Seminars and Symposia | Past seminars/symposia: Wednesday, February 20, 2013

DTC Leading Edge Seminar Series

Detecting Anomalies in a Data Set Under Multiple Criteria

by

Kevin Xu
3M

Wednesday, February 20, 2013
3:30 p.m. reception
4:00 p.m. seminar

401/402 Walter Library

View presentation (PDF 4MB) XuIn this talk I consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. Most anomaly detection algorithms involve calculating dissimilarities between data samples using a criterion such as Euclidean distance. However, in many applications involving complex high-dimensional data, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such a case, multiple criteria for anomaly detection can be defined. I introduce a novel multi-criteria anomaly detection method using Pareto depth analysis (PDA). PDA uses the concept of Pareto optimality to detect anomalies under multiple criteria. I demonstrate the PDA method for detection of anomalous pedestrian trajectories.

 

Kevin S. Xu is a Senior Research Scientist in the Computational Intelligence laboratory at 3M. He performs research and development in statistical signal processing and machine learning with applications to security and health care among other fields. He received the BASc in Electrical Engineering from the University of Waterloo (2007) and the MSE and PhD in Electrical Engineering: Systems from the University of Michigan (2012). He was a recipient of the Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship.