Eigenanalysis of Covariance Matrix: Optimal Subspace Method for Bigdata Applications



by Prof. Ali Naci Akansu, Ph.D.,
New Jersey Institute of Technology
Department of Electrical and Computer Engineering
Newark, NJ 07102 USA
E-mail: ali.n.akansu@njit.edu

Analysis of random vector (stochastic) processes through their covariance matrices has been of a great importance in statistics and engineering. Eigenanalysis of such a matrix offers a powerful mathematical tool to calculate its eigenvectors and eigenvalues that are considered as fundamental characteristics and features of a process. Their use and interpretation have been the core of applications spanning from automated facial emotion analysis and Internet searches to design of optimal investment portfolios. These emerging data intensive (bigdata) applications have increased the R&D activity in principal component analysis (PCA) methods and their implementation. This talk will highlight recent developments in the field and provide new insights in the interpretation of eigenanalysis for various applications.

Biography: Ali N. Akansu received his B.S. degree from the Technical University of Istanbul, Turkey, M.S. and Ph.D. degrees from the Polytechnic University, Brooklyn, New York, all in Electrical Engineering. He has been with the Department of Electrical & Computer Engineering at the New Jersey Institute of Technology where he is Professor of Electrical & Computer Engineering. He was a Founding Director of the New Jersey Center for Multimedia Research and NSF Industry-University Cooperative Research Center for Digital Video. Dr. Akansu was the Vice President for Research and Development of IDT Corporation [NYSE:IDT]. He was the founding President and CEO of PixWave, Inc., and Senior VP for Technology Development of TV.TV (IDT Entertainment subsidiaries). He had been on the boards of several companies and an investment fund. He visited David Sarnoff Research Center, IBM T.J. Watson Research Center, GEC-Marconi Electronic Systems Corp., and Courant Institute of Mathematical Sciences at the New York University. Dr. Akansu has published numerous articles and several books on his research work. His current professional and research interests include theories of signals and transforms, quantitative finance and high frequency trading, bigdata finance, high performance DSP and computing. His two recent books are entitled A Primer for Financial Engineering: Financial Signal Processing & Electronic Trading, Elsevier, 2015, and Financial Signal Processing and Machine Learning, Wiley-IEEE Press, 2016. He is a Fellow of the IEEE.