2015
Approximating Sparse PCA from Incomplete Data
NeurIPS 2015poster
We study how well one can recover sparse principal componentsof a data matrix using a sketch formed from a few of its elements. We show that for a wide class of optimization problems,if the sketch is close (in the spectral norm) to the original datamatrix, then one can recover a near optimal solutio…