Cover Image

Cluster-Based Generalized Additive Models Informed by Random Fourier Features

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Xin, Li, Jia, Yu, Jun
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhancing Kernel Power K-means: Scalable and Robust Clustering with Random Fourier Features and Possibilistic Method
by: Chen, Yixi, et al.
Published: (2025)

End-to-end Kernel Learning via Generative Random Fourier Features
by: Fang, Kun, et al.
Published: (2020)

Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models
by: Jia, Jingyun, et al.
Published: (2026)

Random Fourier Signature Features
by: Toth, Csaba, et al.
Published: (2023)

ANOVA-boosting for Random Fourier Features
by: Potts, Daniel, et al.
Published: (2024)

Fourier Feature Pyramids for Physics-Informed Neural Networks
by: Zhao, Brandon, et al.
Published: (2026)

The Most Important Features in Generalized Additive Models Might Be Groups of Features
by: Bosschieter, Tomas M., et al.
Published: (2025)

Invertible Kernel PCA with Random Fourier Features
by: Gedon, Daniel, et al.
Published: (2023)

Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel Derivatives
by: Wagner, Theresa, et al.
Published: (2024)

Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
by: Wu, Yulun, et al.
Published: (2025)

An Adaptive Random Fourier Features approach Applied to Learning Stochastic Differential Equations
by: Douglas, Owen, et al.
Published: (2025)

RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
by: Otto, Mateus P., et al.
Published: (2022)

Adaptive Random Fourier Features Training Stabilized By Resampling With Applications in Image Regression
by: Kammonen, Aku, et al.
Published: (2024)

Weak-to-Strong Generalization Even in Random Feature Networks, Provably
by: Medvedev, Marko, et al.
Published: (2025)

Physics-Informed Neural Networks with Fourier Features and Attention-Driven Decoding
by: Arni, Rohan, et al.
Published: (2025)

General Fourier Feature Physics-Informed Extreme Learning Machine (GFF-PIELM) for High-Frequency PDEs
by: Ren, Fei, et al.
Published: (2025)

Generalization Bound for Diffusion Models using Random Features
by: Saha, Esha, et al.
Published: (2023)

General Graph Random Features
by: Reid, Isaac, et al.
Published: (2023)

Pre-trained Large Language Models Use Fourier Features to Compute Addition
by: Zhou, Tianyi, et al.
Published: (2024)

NeurCAM: Interpretable Neural Clustering via Additive Models
by: Upadhya, Nakul, et al.
Published: (2024)

Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process
by: Saito, Issei, et al.
Published: (2025)

Preconditioned Additive Gaussian Processes with Fourier Acceleration
by: Wagner, Theresa, et al.
Published: (2025)

Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity
by: Jia, Zihang, et al.
Published: (2024)

Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
by: Shi, Xinxing, et al.
Published: (2024)

Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
by: Choi, Ikjun, et al.
Published: (2024)

On the Generalization Properties of Learning the Random Feature Models with Learnable Activation Functions
by: Ma, Zailin, et al.
Published: (2025)

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
by: Blesch, Kristin, et al.
Published: (2025)

Plastic Learning with Deep Fourier Features
by: Lewandowski, Alex, et al.
Published: (2024)

Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features
by: Kammonen, Aku, et al.
Published: (2024)

Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
by: Cheema, Talay M, et al.
Published: (2023)

FlagGAM: Rule-Based Generalized Additive Modeling for Explainable Tabular Prediction
by: Zhao, Zijie, et al.
Published: (2026)

Randomized Quasi-Monte Carlo Features for Kernel Approximation
by: Huang, Yian, et al.
Published: (2025)

Feature Incremental Clustering with Generalization Bounds
by: Zhang, Jing, et al.
Published: (2026)

Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions
by: Kim, Minkyu, et al.
Published: (2022)

Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
by: Basterrech, Sebastian, et al.
Published: (2025)

Regular Fourier Features for Nonstationary Gaussian Processes
by: Jawaid, Arsalan, et al.
Published: (2026)

Understanding Robust Overfitting from the Feature Generalization Perspective
by: Yu, Chaojian, et al.
Published: (2023)

One-Bit Quantization for Random Features Models
by: Akhtiamov, Danil, et al.
Published: (2025)

FoNE: Precise Single-Token Number Embeddings via Fourier Features
by: Zhou, Tianyi, et al.
Published: (2025)

A General Anchor-Based Framework for Scalable Fair Clustering
by: Wei, Shengfei, et al.
Published: (2025)