Statistics Seminar: STA 290
Wednesday, November 9th, 2011 at 1.10pm, MSB 1147 (Colloquium Room)
Speaker: Raj Rao Nadakuditi (University of Michigan)
Title: Random matrix theory and the informational limit of eigen-analysis
Abstract: Motivated by signal-plus-noise type models in high-dimensional
statistical signal processing and machine learning, we consider the eigenvalues
and eigenvectors of finite, low rank perturbations of large random matrices.
Applications in mind are as diverse as radar, sonar, wireless communications,
spectral clustering, bio-informatics and Gaussian mixture cluster analysis in
We provide an application-independent approach that brings into sharp focus a
fundamental informational limit of high-dimensional eigen-analysis. Continuing
on this success, we highlight the random matrix origin of this informational
limit, the connection with "free" harmonic analysis and discuss
implications for high-dimensional statistical signal processing and learning.