Tensor-Train Networks for Learning Predictive Modeling of Multidimensional Data Article Swipe
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· 2021
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2101.09184
In this work, we firstly apply the Train-Tensor (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 a 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.
Related Topics To Compare & Contrast
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2101.09184
- https://arxiv.org/pdf/2101.09184
- OA Status
- green
- Cited By
- 2
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3121472434