26th European Signal Processing Conference (EUSIPCO)
Abstract
We address the problem of entry-wise low-rank matrix completion in the noisy observation model. We propose a new noise robust estimator where we characterize the bias and variance of the estimator in a finite sample setting. Utilizing this estimator, we provide a new robust local matrix completion algorithm that outperforms other classic methods in reconstructing large rectangular matrices arising in a wide range of applications such as athletic performance prediction and recommender systems. The simulation results on synthetic and real data show that our algorithm outperforms other state-of-the-art and baseline algorithms in matrix completion in reconstructing rectangular matrices
