Robust orthogonal matrix factorization for efficient subspace learning

Eunwoo Kim, Songhwai Oh

Abstract: Highlights•This paper considers a subspace learning problem in the presence of corruptions.•The proposed method finds a robust solution using orthogonality and smoothness constraints.•The proposed method can handle missing or unknown entries as well as outliers.•The proposed method is extended to handle the rank uncertainty issue.•We demonstrate that the proposed method is robust for various subspace learning problems.