{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/eigengp-gaussian-process-models-with-adaptive","title":"EigenGP: Gaussian Process Models with Adaptive Eigenfunctions","arxiv_id":"1401.0362","date":"2014-01-02","proceeding":null,"authors":["Hao Peng","Yuan Qi"],"abstract":"Gaussian processes (GPs) provide a nonparametric representation of functions.\nHowever, classical GP inference suffers from high computational cost for big\ndata. In this paper, we propose a new Bayesian approach, EigenGP, that learns\nboth basis dictionary elements--eigenfunctions of a GP prior--and prior\nprecisions in a sparse finite model. It is well known that, among all\northogonal basis functions, eigenfunctions can provide the most compact\nrepresentation. Unlike other sparse Bayesian finite models where the basis\nfunction has a fixed form, our eigenfunctions live in a reproducing kernel\nHilbert space as a finite linear combination of kernel functions. We learn the\ndictionary elements--eigenfunctions--and the prior precisions over these\nelements as well as all the other hyperparameters from data by maximizing the\nmodel marginal likelihood. We explore computational linear algebra to simplify\nthe gradient computation significantly. Our experimental results demonstrate\nimproved predictive performance of EigenGP over alternative sparse GP methods\nas well as relevance vector machine.","url_abs":"http://arxiv.org/abs/1401.0362v3","url_pdf":"http://arxiv.org/pdf/1401.0362v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"eigengp-gaussian-process-models-with-adaptive","repo_url":"https://github.com/hao-peng/EigenGP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}