Abstract

In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas, including computer vision with applications in affifine structure-from-motion, photometric stereo, and non-rigid structure from motion. We specififically target structured data patterns, with outliers and large amounts of missing data. Using recently developed characterizations of minimal solutions to matrix factorization problems with missing data, we show how these can be used as building blocks in a hierarchical system that performs bootstrapping on all levels. This gives a robust and fast system, with state-of-the-art performance.