Quadrature-based features for kernel approximation

2018/02/11 by Marina Munkhoeva, Yermek Kapushev, Munkhoeva, Marina +5 · 2 citations
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks

paper · pdf · doi:10.48550/arxiv.1802.03832

openalex publication_date 2018/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on an efficient numerical integration technique, we propose a unifying approach that reinterprets the previous random features methods and extends to better estimates of the kernel approximation. We derive the convergence behaviour and conduct an extensive empirical study that supports our hypothesis.

Citations

• Compact Nonlinear Maps and Circulant Extensions
• Learning Multiple Layers of Features from Tiny Images

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