{"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/mixed-likelihood-gaussian-process-latent","title":"Mixed Likelihood Gaussian Process Latent Variable Model","arxiv_id":"1811.07627","date":"2018-11-19","proceeding":null,"authors":["Samuel Murray","Hedvig Kjellström"],"abstract":"We present the Mixed Likelihood Gaussian process latent variable model\n(GP-LVM), capable of modeling data with attributes of different types. The\nstandard formulation of GP-LVM assumes that each observation is drawn from a\nGaussian distribution, which makes the model unsuited for data with e.g.\ncategorical or nominal attributes. Our model, for which we use a sampling based\nvariational inference, instead assumes a separate likelihood for each observed\ndimension. This formulation results in more meaningful latent representations,\nand give better predictive performance for real world data with dimensions of\ndifferent types.","url_abs":"http://arxiv.org/abs/1811.07627v1","url_pdf":"http://arxiv.org/pdf/1811.07627v1.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":"mixed-likelihood-gaussian-process-latent","repo_url":"https://github.com/samuelmurray/heterogeneous-gp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}