Spring 2015 ECE RESEARCH SEMINAR Distributed Data Clustering and Cleansing in Sensor Networks Dr. Ioannis Schizas Dept. of Electrical Engineering, Univ. of Texas at Arlington Friday, March 27, 2015, 2:00pm – 2:50pm, Location: BB 3.04.18 Abstract The problem of identifying informative sensors that acquire measurements about multiple sources and clustering them according to their source content is considered in this presentation. Toward this end, a novel canonical correlation analysis (CCA) framework equipped with sparsity-‐inducing norm-‐one regularization is introduced to identify correlated sensor measurements and identify informative groups of sensors. It is established that the novel framework is capable to cluster sensors, based on their source content, correctly (with probability one) even in nonlinear settings and when sources do not overlap. Block coordinate techniques are employed to derive a centralized algorithm that minimizes the sparsity-‐aware CCA framework. The latter framework is reformulated as a separable optimization program which is tackled in a distributed fashion via the alternating direction method of multipliers. Further, a distributed framework is put forth that enables sensors to extract the `clean' portion of a data sequence and isolate corrupted data. Different from outliers, the corrupted data may affect an arbitrary in size portion of the data sequence. This is achieved by combining low-‐rank matrix decomposition and data selection. Bio Ioannis D. Schizas received the Diploma degree in Computer Engineering and Informatics from the University of Patras, Greece, in 2004, the M.Sc. degree in Electrical and Computer Engineering from the University of Minnesota, Minneapolis, in 2007, and the Ph.D. degree in Electrical and Computer Engineering from the University of Minnesota, Minneapolis, in June 2011. Since August 2011, he has been an Assistant Professor at the Electrical Engineering department at the University of Texas at Arlington. His general research interests lie in the areas of statistical signal processing, wireless sensor networks and data dimensionality reduction. His recent work focuses on distributed signal processing using wireless ad hoc sensor networks, as well as sparsity-‐aware information processing with applications on data mining and dimensionality reduction. For more information, visit ECE website: Home > Seminar (http://www.ece.utsa.edu) Contact: Nikolaos Gatsis (nikolaos.gatsis@utsa.edu, x5519)