Adaptive Basis Function Selection for Computationally Efficient Predictions

Anton Kullberg

doi:10.1109/LSP.2024.3445272

Adaptive Basis Function Selection for Computationally Efficient Predictions

2024-01-01

0

OA

AI

DOI：10.1109/LSP.2024.3445272

Original

Original request for help

Save

Abstract

En 中文

Basis Function (BF) expansions are a cornerstone of any engineer's toolbox for computational function approximation which shares connections with both neural networks and Gaussian processes. Even though BF expansions are an intuitive and straightforward model to use, they suffer from quadratic computational complexity in the number of BFs if the predictive variance is to be computed. We develop a method to automatically select the most important BFs for prediction in a sub-domain of the model domain. This significantly reduces the computational complexity of computing predictions while maintaining predictive accuracy. The proposed method is demonstrated using two numerical examples, where reductions up to 50-75% are possible without significantly reducing the predictive accuracy.

Keywords:

Computational modeling

Predictive models

Adaptation models

Data models

Accuracy

Training data

Probabilistic logic

Adaptive signal processing

computational complexity

function approximation

Gaussian processes

Adaptive Basis Function Selection for Computationally Efficient Predictions

Abstract

Journal

Organization