Low-rank matrix completion using alternating minimization
Low-rank matrix completion using alternating minimization is a scholarly work, published in 2013. The main subjects of the publication include low-rank approximation, minification, compressed sensing, rank, image segmentation, matrix completion, component, super-resolution imaging, computer science, mathematical optimization, matrix, and algorithm. Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data.