Tensor-Train networks for learning predictive modeling of multidimensional data

Autor(es)

da Costa, Nazareth ; Attux, Romis ; Cichocki, Andrzej ; Romano, Joao M. T.

• Tipo de documento: Artigo Científico
• Fonte: Neurocomputing; v. 637, p. 22-pg., 2025-07-07.

Resumo

In this work, we firstly apply Tensor-Train (TT) networks to construct a compact representation of the classical Multilayer Perceptron, representing a reduction of up to 95% of the coefficients. A comparative analysis between tensor model and standard multilayer neural networks is also carried out in the context of prediction of the Mackey-Glass noisy chaotic time series and NASDAQ index. We show that the weights of a multidimensional regression model can be learned by means of TT network and the optimization of TT weights is more robust to the impact of coefficient initialization and hyper-parameter setting. Furthermore, an efficient algorithm based on alternating least squares has been proposed for approximating the weights in TT format with a reduction of computational calculus, providing a much faster convergence than the well-known adaptive learning-method algorithms, widely applied for optimizing neural networks. (AU)

• Processo FAPESP: 14/23936-4 - Aplicações de processamento de dados multidimensionais usando métodos tensoriais
• Beneficiário: Michele Nazareth da Costa
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Beneficiário: João Marcos Travassos Romano
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada