Partial estimation of covariance matrices (1008.1716v2)

Abstract: A classical approach to accurately estimating the covariance matrix \Sigma of a p-variate normal distribution is to draw a sample of size n > p and form a sample covariance matrix. However, many modern applications operate with much smaller sample sizes, thus calling for estimation guarantees in the regime n << p. We show that a sample of size n = O(m log^6 p) is sufficient to accurately estimate in operator norm an arbitrary symmetric part of \Sigma consisting of m < n entries per row. This follows from a general result on estimating Hadamard products M.\Sigma, where M is an arbitrary symmetric matrix.