1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
Webinar Details: Wednesday, 13 December 2017 from 2:30pm - 3:30pm (New York time)
Attendee Registration Link: Register for Webinar, by Dr. Nikos Sidiropoulos
Abstract: We reveal an interesting link between tensors and multivariate statistics. The rank of a multivariate probability tensor can be interpreted as a nonlinear measure of statistical dependence of the associated random variables. Rank equals one when the random variables are independent, and complete statistical dependence corresponds to full rank; but we show that rank as low as two can already model strong statistical dependence. In practice, we usually work with random variables that are neither independent nor fully dependent—partial dependence is typical, and can be modeled using a low-rank multivariate probability tensor. Directly estimating such a tensor from sample averages is impossible even for as few as ten random variables taking ten values each—yielding a billion unknowns; but we often have enough data to estimate lower-order marginalized distributions. We prove that it is possible to identify the higher-order joint probabilities from lower order ones, provided that the higher-order probability tensor has low-enough rank, i.e., the random variables are only partially dependent. We also provide a computational identification algorithm that is shown to work well on both simulated and real data. The insights and results have numerous applications in estimation, hypothesis testing, completion, machine learning, and system identification. Low-rank tensor modeling thus provides a “universal” non-parametric (model-free) alternative to probabilistic graphical models.
Nikos Sidiropoulos received the Diploma in Electrical Engineering from the Aristotelian University of Thessaloniki, Greece, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland–College Park, in 1988, 1990, and 1992, respectively.
Dr. Sidiropoulos received the NSF/CAREER award in 1998, and the IEEE Signal Processing Society (SPS) Best Paper Award in 2001, 2007, and 2011. He served as IEEE SPS Distinguished Lecturer (2008–2009), and as Chair of the IEEE Signal Processing for Communications and Networking Technical Committee (2007–2008), and was recently elected Vice President - Membership of the IEEE Signal Processing Society. He received the 2010 IEEE Signal Processing Society Meritorious Service Award, and the 2013 Distinguished Alumni Award from the University of Maryland, Department of Electrical and Computer Engineering. He is a Fellow of IEEE (2009) and a Fellow of EURASIP (2014).
|Call for Nominations for Editor-in-Chief||5 April 2021|
|Call for Nominations: Chief Editor, SigPort and Chief Editor, Resource Center||5 April 2021|
|Call for Nominations: Board of Governors Members-at-Large and Regional Directors-at-Large||7 April 2021|
|Extended Deadline - 23 April: Call for Nominations for Editors-in-Chief||23 April 2021|
|Call for Nominations: Distinguished Industry Speakers and Distinguished Lecturers||31 May 2021|
|Call for Nominations: Director-Student Services, Director-Membership Development, and Seasonal Schools Subcommittee Chair||4 June 2021|
© Copyright 2021 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.