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arXiv Machine Learning By Marnix Van Soom, Ivan De Boi

On Basis Function Selection for Sparse Gaussian Process Regression

Read the original on arXiv Machine Learning →

The paper proposes three information‑theoretic criteria for selecting the most relevant basis functions in sparse Gaussian process regression, tailored to different levels of prior knowledge. Experiments on six UCI regression datasets and three basis families (HSGP, VFF, VISH) show that the no‑data criterion is a robust default, often outperforming simple truncation, while the data‑aware criteria yield significant improvements for HSGP. The study demonstrates that careful basis‑function selection can lead to better performance without increasing computational cost.

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