Multi-reference factor analysis: low-rank covariance estimation under unknown translations

Abstract We consider the problem of estimating the covariance matrix of a random signal observed through unknown translations (modeled by cyclic shifts) and corrupted by noise. Solving this problem allows to discover low-rank structures masked by the existence of translations (which act as nuisance parameters), with direct application to principal components analysis. We assume that the underlying signal is of length $L$ and follows a standard factor model with mean zero and $r$ normally distributed factors. To recover the covariance matrix in this case, we propose to employ the second- and fourth-order shift-invariant moments of the signal known as the power spectrum and the trispectrum. We prove that they are sufficient for recovering the covariance matrix (under a certain technical condition) when $r