CSP Seminar

Random matrix theory and the informational limit of eigen-analysis

Raj Rao Nadakuditi

Assistant Professor
University of Michigan - Department of EECS
 
Thursday, November 21, 2013
4:00pm - 5:00pm
1005 EECS

 

About the Event

Motivated by the ubiquity of 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 machine learning. We provide an application-independent approach that brings into sharp focus a fundamental informational limit of high-dimensional eigen-analysis. Building on this success, we highlight the random matrix origin of this informational limit, the connection with "free" harmonic analysis and discuss how to exploit these insights to improve low-rank signal matrix denoising relative to the truncated SVD.

Additional Information

Contact: Ann Pace

Phone: 763-5022

Email: ampace@umich.edu

Sponsor: University of Michigan, Department of Electrical Engineering & Computer Science

Open to: Public