N. Sidiropoulos is the Louis T. Rader Professor and Chair of the ECE Department at the University of Virginia. He earned his Ph.D. in Electrical Engineering from the University of Maryland–College Park, in 1992. He has served on the faculty of the University of Minnesota, and the Technical University of Crete, Greece. His research interests are in signal processing, communications, optimization, tensor decomposition, and factor analysis, with applications in machine learning and communications. He received the NSF/CAREER award in 1998, the IEEE Signal Processing Society (SPS) Best Paper Award in 2001, 2007, and 2011, has authored a Google Classic Paper, and his tutorial on tensor decomposition is ranked #1 in Google Scholar metrics for IEEE Transactions in Signal Processing (TSP), and also tops the charts of the most popular / most frequently accessed TSP papers in IEEExplore. He served as IEEE SPS Distinguished Lecturer (2008-2009), and as Vice President of IEEE SPS. He received the 2010 IEEE Signal Processing Society Meritorious Service Award, and the 2013 Distinguished Alumni Award from the ECE Department of the University of Maryland. He is a Fellow of IEEE (2009) and a Fellow of EURASIP (2014). You can find more information here and here
Nonparametric Multivariate Density Estimation: A Low-Rank Characteristic Function Approach
Effective non-parametric density estimation is a key challenge in high-dimensional multivariate data analysis. Building upon our recent work, which addressed the case of categorical / finite-alphabet random vectors, we propose a novel approach that leverages tensor factorization tools for continuous density estimation. Any multivariate density can be represented by its characteristic function, via the Fourier transform. If the sought density is compactly supported, then its characteristic function can be approximated, within controllable error, by a finite tensor of leading Fourier coefficients, whose size depends on the smoothness of the underlying density. This tensor can be naturally estimated from observed realizations of the random vector of interest, via sample averaging. In order to circumvent the curse of dimensionality, we introduce a low-rank model of this characteristic tensor, which significantly improves the density estimate especially for high-dimensional data and/or in the sample-starved regime. By virtue of uniqueness of low-rank tensor decomposition, under certain conditions, our method enables learning the true data-generating distribution. We will demonstrate the very promising performance of the proposed method using several measured datasets.