Abstract
We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of variables based on their predictive relevance. Our results on synthetic and real world datasets show that the proposed method improves the assessment of variable relevance compared to the automatic relevance determination (ARD) via the length-scale parameters. We expect the method to be useful for improving explainability of the models, reducing the future measurement costs and reducing the computation time for making new predictions.
| Original language | English |
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| Title of host publication | 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) |
| Publisher | IEEE |
| ISBN (Electronic) | 978-1-5090-0746-2 |
| DOIs | |
| Publication status | Published - 2016 |
| MoE publication type | A4 Article in a conference publication |
| Event | IEEE International Workshop on Machine Learning for Signal Processing - Salerno, Italy Duration: 13 Sep 2016 → 16 Sep 2016 Conference number: 26 http://mlsp2016.conwiz.dk/home.htm |
Publication series
| Name | IEEE International Workshop on Machine Learning for Signal Processing |
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| ISSN (Print) | 2161-0371 |
| ISSN (Electronic) | 2161-0363 |
Workshop
| Workshop | IEEE International Workshop on Machine Learning for Signal Processing |
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| Abbreviated title | MLSP |
| Country/Territory | Italy |
| City | Salerno |
| Period | 13/09/2016 → 16/09/2016 |
| Internet address |