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

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Abstract

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.

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Notes

  1. http://archive.ics.uci.edu/ml/datasets/EMG+Physical+Action+Data+Set.

  2. http://archive.ics.uci.edu/ml/datasets/EMG+dataset+in+Lower+Limb.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61976092 and 61473123), Guangdong Basic and Applied Basic Research Foundation (Nos. 2021A1515011317, 2017A030313370, and 2018A030313356), and Guangdong Key Area Research and Development Plan (No. 2020B1111120001).

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  1. Qi Tan and Pei Yang have contributed equally.

Authors and Affiliations

  1. South China Normal University, Guangzhou, China

    Qi Tan

  2. South China University of Technology, Guangzhou, China

    Pei Yang & Guihua Wen

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  1. Qi Tan
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  2. Pei Yang
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  3. Guihua Wen
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Correspondence to Pei Yang.

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Tan, Q., Yang, P. & Wen, G. Deep non-negative tensor factorization with multi-way EMG data. Neural Comput & Applic 34, 1307–1317 (2022). https://doi.org/10.1007/s00521-021-06474-w

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