  Low-rank tensor recovery problems have been widely studied in many
applications of signal processing and machine learning. Tucker decomposition is
known as one of the most popular decompositions in the tensor framework. In
recent years, researchers have developed many state-of-the-art algorithms to
address the problem of low-Tucker-rank tensor recovery. Motivated by the
favorable properties of the stochastic algorithms, such as stochastic gradient
descent and stochastic iterative hard thresholding, we aim to extend the
well-known stochastic iterative hard thresholding algorithm to the tensor
framework in order to address the problem of recovering a low-Tucker-rank
tensor from its linear measurements. We have also developed linear convergence
analysis for the proposed method and conducted a series of experiments with
both synthetic and real data to illustrate the performance of the proposed
method.
