{"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/standing-wave-decomposition-gaussian-process","title":"Standing Wave Decomposition Gaussian Process","arxiv_id":"1803.03666","date":"2018-03-09","proceeding":null,"authors":["Chi-Ken Lu","Scott Cheng-Hsin Yang","Patrick Shafto"],"abstract":"We propose a Standing Wave Decomposition (SWD) approximation to Gaussian\nProcess regression (GP). GP involves a costly matrix inversion operation, which\nlimits applicability to large data analysis. For an input space that can be\napproximated by a grid and when correlations among data are short-ranged, the\nkernel matrix inversion can be replaced by analytic diagonalization using the\nSWD. We show that this approach applies to uni- and multi-dimensional input\ndata, extends to include longer-range correlations, and the grid can be in a\nlatent space and used as inducing points. Through simulations, we show that our\napproximate method applied to the squared exponential kernel outperforms\nexisting methods in predictive accuracy per unit time in the regime where data\nare plentiful. Our SWD-GP is recommended for regression analyses where there is\na relatively large amount of data and/or there are constraints on computation\ntime.","url_abs":"http://arxiv.org/abs/1803.03666v4","url_pdf":"http://arxiv.org/pdf/1803.03666v4.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":"standing-wave-decomposition-gaussian-process","repo_url":"https://github.com/CoDaS-Lab/LG-SWD-GP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}