Convex Low Rank Approximation
Low rank approximation is an important tool in many applications. Given an observed matrix with elements corrupted by Gaussian noise it is possible to find the best approximating matrix of a given rank through singular value decomposition. However, due to the non-convexity of the formulation it is not possible to incorporate any additional knowledge of the sought matrix without resorting to heuris
