Introduction to the non-asymptotic analysis of random matrices

Details Introduction to the non-asymptotic analysis of random matrices Introduction to the non-asymptotic analysis of random matrices

2010-11-12

arxiv.org/abs/1011.3027v7

Chapter 5 of: Compressed Sensing, Theory and Applications. Edited by Y. Eldar and G. Kutyniok. Cambridge University Press, 201

Mathematics

Probability

62 pages. Minor corrections

Scientific paper

This is a tutorial on some basic non-asymptotic methods and concepts in random matrix theory. The reader will learn several tools for the analysis of the extreme singular values of random matrices with independent rows or columns. Many of these methods sprung off from the development of geometric functional analysis since the 1970's. They have applications in several fields, most notably in theoretical computer science, statistics and signal processing. A few basic applications are covered in this text, particularly for the problem of estimating covariance matrices in statistics and for validating probabilistic constructions of measurement matrices in compressed sensing. These notes are written particularly for graduate students and beginning researchers in different areas, including functional analysts, probabilists, theoretical statisticians, electrical engineers, and theoretical computer scientists.

Affiliated with

Also associated with

No associations

Rating

Introduction to the non-asymptotic analysis of random matrices does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Introduction to the non-asymptotic analysis of random matrices, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Introduction to the non-asymptotic analysis of random matrices will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-444647