Deep non-negative tensor factorization with multi-way EMG data

Tensor decomposition is widely used in a variety of applications such as data mining, biomedical informatics, neuroscience, and signal processing. In this paper, we propose a deep non-negative tensor factorization (DNTF) model to learn intrinsic and hierarchical structures from multi-way data. The DNTF model takes the Tucker tensor decomposition as a building block to stack up a multi-layer structure. In such a way, we can gradually learn the more abstract structures in a higher layer. The benefit is that it helps to mine intrinsic correlations and hierarchical structures from multi-way data. The non-negative constraints allow for clustering interpretation of the extracted data-dependent components. The objective of DNTF is to minimize the total reconstruction loss resulting from using the core tensor in the highest layer and the mode matrices in each layer to reconstruct the data tensor. Then, a deep decomposition algorithm based on multiplicative update rules is proposed to solve the optimization problem. It first conducts layer-wise tensor factorization and then fine-tunes the weights of all layers to reduce the total reconstruction loss. The experimental results on biosignal sensor data demonstrate the effectiveness and robustness of the proposed approach.