Fully connected-based nonnegative matrix factorization neural network

Shengnan Liu; Xiaoge Wei; Lijun Yang

doi:10.1117/12.3006004

10 October 2023 Fully connected-based nonnegative matrix factorization neural network

Shengnan Liu, Xiaoge Wei, Lijun Yang

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279928 (2023) https://doi.org/10.1117/12.3006004
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Nonnegative matrix factorization (NMF) is a powerful method for feature extraction, offering explanatory and dimensionality reduction. Alternatively, combining NMF with a neural network requires iterative optimization of an objective function, followed by constructing a specialized neural network based on the derived formula. The interpretability and universality of this approach are limited. To address these issues, this paper introduces a novel model called FCNMFN, which leverages a fully connected neural network to implement NMF. In this model, each layer of the fully connected neural network corresponds to the transpose of the base matrix, the coefficient matrix, and the sample matrix of NMF. This design ensures strong interpretability while achieving nonnegative matrix factorization. To demonstrate the effectiveness of the proposed model, we apply it to emotion recognition using the DEAP dataset. Experimental results confirm its efficacy and showcase its potential in accurately identifying and analyzing emotions.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shengnan Liu, Xiaoge Wei, and Lijun Yang "Fully connected-based nonnegative matrix factorization neural network", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279928 (10 October 2023); https://doi.org/10.1117/12.3006004

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