Applied Mathematics Colloquium
Annenberg 105
Random matrix theory and the informational limit of eigen-analysis
Raj Rao Nadakuditi,
Assistant Professor,
EECS,
University of Michigan,
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 machine learning. 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.
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. 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.
For more information, please contact Sydney Garstang by phone at x4555 or by email at [email protected] or visit http://www.acm.caltech.edu.
Event Series
Applied Mathematics Colloquium Series