Robust low-rank tensor modelling using Tucker and CP decomposition.

A framework for reliable seperation of a low-rank subspace from grossly corrupted multi-dimensional signals is pivotal in modern signal processing applications. Current methods fall short of this separation either due to the radical simplification or the drastic transformation of data. This has motivated us to propose two new robust low-rank tensor models: Tensor Orthonormal Robust PCA (TORCPA) and Tensor Robust CP Decomposition (TRCPD). They seek Tucker and CP decomposition of a tensor respectively with l(p) norm regularisation. We compare our methods with state-of-the-art low-rank models on both synthetic and real-world data. Experimental results indicate that the proposed methods are faster and more accurate than the methods they compared to.