Low Rank Activations For Tensor-Based Convolutional Sparse Coding

In this article, we propose to extend the classical Convolutional Sparse Coding model (CSC) to multivariate data by introducing a new tensor CSC model that enforces sparsity and low-rank constraint on the activations. The advantages of this model are threefold. First, by using tensor algebra, this model takes into account the underlying structure of the data. Second, this models allows for complex atoms but enforces fewer activations to decompose the data, resulting in an improved summary (dictionary) and a better reconstruction of the original multivariate signal. Third, the number of parameters to be estimated are greatly reduced by the low-rank constraint. We exhibit the associated optimization problem and propose a framework based on alternating optimization to solve it. Finally, we evaluate it on both synthetic and real data.