Polynomial Feature

Polynomial features are used in machine learning to enhance model expressiveness by mapping data into higher-dimensional spaces, enabling the fitting of complex, non-linear relationships. Current research focuses on improving the efficiency and accuracy of polynomial feature methods, particularly within neural networks (e.g., Polynomial-Augmented Neural Networks) and tensor network models, often incorporating techniques like orthogonality constraints and feature quantization to mitigate the curse of dimensionality. These advancements are significant because they improve the accuracy and interpretability of models across various applications, including medical image segmentation, solving partial differential equations, and regression tasks, while also addressing computational limitations associated with high-dimensional feature spaces.