Robust Low-Rank Matrix Completion by Riemannian Optimization
Robust Low-Rank Matrix Completion by Riemannian Optimization is a scholarly work by Pierre-Antoine Absil, published in 2016 in ''SIAM Journal on Scientific and Statistical Computing''. The main subjects of the publication include mathematics, Gaussian, super-resolution imaging, noise reduction, compressed sensing, Riemannian optimization, mathematical optimization, applied mathematics, algorithm, outlier, matrix norm, matrix completion, rank, smoothing, low-rank approximation, and matrix. The authors propose RMC, a new method to deal with the problem of robust low-rank matrix completion, i.e., matrix completion where a fraction of the observed entries are corrupted by non-Gaussian noise, typically outliers.