“…D EEP LEARNING (DL) and neural networks (NNs) [1] are among the most popular techniques of machine learning (ML), and are broadly used in signal processing, among other disciplines [2]- [5]. However, the umbrella of ML covers many other techniques, such as support vector machines [6], [7], kernel adaptive filters [8], [9], random forests [10], [11] and tensor-based estimators [12]. Although it has been shown that tensor-based methods can deliver on par, or even better performance than other methods [13], [14], and can be used in a variety of applications [12], [15]- [20], they are usually disregarded due to their high memory-and computational footprint needed to approximate a given system.…”