Cluster-Based Generalized Additive Models Informed by Random Fourier Features

• URL: http://arxiv.org/abs/2512.19373v1
• Date: Mon, 22 Dec 2025 13:15:52 GMT
• Title: Cluster-Based Generalized Additive Models Informed by Random Fourier Features
• Authors: Xin Huang, Jia Li, Jun Yu,
• Abstract summary: This work introduces a mixture of generalized additive models (GAMs) in which random Fourier feature (RFF) representations are leveraged to uncover locally adaptive structure in the data.<n> Numerical experiments on real-world regression benchmarks, including the California Housing, NASA Air Self-Noise, and Bike Sharing datasets, demonstrate improved predictive performance.
• Score: 19.409397281817288
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Explainable machine learning aims to strike a balance between prediction accuracy and model transparency, particularly in settings where black-box predictive models, such as deep neural networks or kernel-based methods, achieve strong empirical performance but remain difficult to interpret. This work introduces a mixture of generalized additive models (GAMs) in which random Fourier feature (RFF) representations are leveraged to uncover locally adaptive structure in the data. In the proposed method, an RFF-based embedding is first learned and then compressed via principal component analysis. The resulting low-dimensional representations are used to perform soft clustering of the data through a Gaussian mixture model. These cluster assignments are then applied to construct a mixture-of-GAMs framework, where each local GAM captures nonlinear effects through interpretable univariate smooth functions. Numerical experiments on real-world regression benchmarks, including the California Housing, NASA Airfoil Self-Noise, and Bike Sharing datasets, demonstrate improved predictive performance relative to classical interpretable models. Overall, this construction provides a principled approach for integrating representation learning with transparent statistical modeling.

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