TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization

• URL: http://arxiv.org/abs/2307.16105v1
• Date: Sun, 30 Jul 2023 01:52:00 GMT
• Title: TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
• Authors: Andrei Ivanov, Stefan Maria Ailuro
• Abstract summary: The paper presents a method for constructing a high-order regression based on the Taylor map factorization. By benchmarking on UCI open access datasets, we demonstrate that the proposed method performs comparable to the state-of-the-art regression methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Polynomial regression is widely used and can help to express nonlinear patterns. However, considering very high polynomial orders may lead to overfitting and poor extrapolation ability for unseen data. The paper presents a method for constructing a high-order polynomial regression based on the Taylor map factorization. This method naturally implements multi-target regression and can capture internal relationships between targets. Additionally, we introduce an approach for model interpretation in the form of systems of differential equations. By benchmarking on UCI open access datasets, Feynman symbolic regression datasets, and Friedman-1 datasets, we demonstrate that the proposed method performs comparable to the state-of-the-art regression methods and outperforms them on specific tasks.

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