Matrix Completion under Interval Uncertainty
This addresses matrix completion problems with interval uncertainty, such as in image in-painting and collaborative filtering, offering incremental improvements in efficiency and performance.
The paper tackles matrix completion with element-wise box constraints, presenting an efficient alternating-direction parallel coordinate-descent method that outperforms other methods on an image in-painting benchmark in signal-to-noise ratio and solves a collaborative filtering instance with 100,198,805 recommendations within 5 minutes.
Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes.