Robust PCA via tensor outlier pursuit
In this paper, we study robust principal component analysis on tensors, in the setting where frame wise outliers exist. We propose a convex formulation to decompose a tensor into a low rank component and a frame wise sparse component. Theoretically, we guarantee that exact subspace recovery and outlier identification can be achieved under mild model assumptions. Compared with entry wise outlier pursuit and naive matricization of tensors with frame wise outliers, our approach can handle higher ranks and proportion of outliers. Extensive numerical evaluations are provided on both synthetic and real data to support our theory.
2016 50th Asilomar Conference on Signals, Systems and Computers