PCA from linearly reduced measurements: the diagonal case

We study the spiked covariance model when the measurements have undergone a diagonal reduction. This includes data corrupted by missing entries and random linear filters. We derive the asymptotic limits of the eigenvalues and principal components for reduced data.

We apply these new results to several statistical problems, including singular value shrinkage. This is joint work with Amit Singer and Edgar Dobriban.