From basis components to complex structural patterns

A novel approach is proposed to extract high-rank patterns from multiway data. The method is useful when signals comprise collinear components or complex structural patterns. Alternating least squares and multiplication algorithms are developed for the new model with/without non negativity constraints. Experimental results on synthetic data and real-world dataset confirm the validity of the proposed model and algorithms.