Matrix completion by Truncated Nuclear Norm Regularization
Matrix completion by Truncated Nuclear Norm Regularization is a scholarly work, published in 2012. The main subjects of the publication include regular polygon, facial recognition system, singular value, matrix completion, compressed sensing, mathematical optimization, matrix, rank, convex optimization, matrix norm, image segmentation, regularization, applied mathematics, algorithm, iterative numerical method, norm, low-rank approximation, computer science, and mathematics. The authors further develop an efficient iterative procedure to solve the optimization problem by using the alternating direction method of multipliers and the accelerated proximal gradient line search method.