Sparsity and Low Rank for Robust Social Data Analytics and Networking
(Lectures and Seminars)
Georgios B. Giannakis, Ph.D.
University of Minnesota
The information explosion propelled by the advent of personal computers, the Internet, and the global communications has rendered statistical learning from 'Big Data' increasingly important. Along with data adhering to postulated models, present in large volumes of data are also those that do not - what are referred to as outliers or anomalies. In this talk, I will start with an approach to outlier-resilient principal component analysis, which establishes a neat link between the seemingly unrelated notions of sparsity and robustness to outliers, even when the signals involved are not sparse. In the second part of the talk, I will switch focus towards the important task of unveiling and mapping-out network anomalies given link-level traffic measurements. If time allows, I will finally highlight additional application domains that include predicting network-wide path latencies, and load curve cleansing and imputation -- a critical task in green grid analytics and energy management with renewables.