2014 Poster Sessions : Exact and Stable Covariance Estimation from Quadratic Sampling via Convex Programming

Student Name : Yuxin Chen
Advisor : Andrea Goldsmith
Research Areas: Information Systems
Abstract:
Statistical inference and information processing of high-dimensional data often require efficient estimation of their second-order statistics. With rapidly changing data, limited processing power and storage at the data acquisition devices, it is desirable to extract the covariance structure from minimal storage. In this paper, we explore a quadratic measurement model which imposes a minimal memory requirement and low computational complexity during the sampling process, and is shown to be optimal in preserving low-dimensional covariance structures. We show that a covariance matrix under appropriate structural assumptions can be perfectly recovered from a near-optimal number of sub-Gaussian quadratic measurements, via efficient convex relaxation algorithms for respective structure.

Bio:
Yuxin Chen is a Ph.D. candidate in the Department of Electrical Engineering at Stanford University, under supervision of Prof. Andrea Goldsmith. He has received the M.S. in Statistics from Stanford University in 2013, the M.S. in Electrical and Computer Engineering from the University of Texas at Austin in 2010, and the B.S. in Microelectronics with High Distinction from Tsinghua University in 2008. His research interests include high-dimensional statistics, information theory, convex analysis, statistical learning, and network science.