{"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/latent-gaussian-processes-for-distribution","title":"Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data","arxiv_id":"1503.02182","date":"2015-03-07","proceeding":null,"authors":["Yarin Gal","Yutian Chen","Zoubin Ghahramani"],"abstract":"Multivariate categorical data occur in many applications of machine learning.\nOne of the main difficulties with these vectors of categorical variables is\nsparsity. The number of possible observations grows exponentially with vector\nlength, but dataset diversity might be poor in comparison. Recent models have\ngained significant improvement in supervised tasks with this data. These models\nembed observations in a continuous space to capture similarities between them.\nBuilding on these ideas we propose a Bayesian model for the unsupervised task\nof distribution estimation of multivariate categorical data. We model vectors\nof categorical variables as generated from a non-linear transformation of a\ncontinuous latent space. Non-linearity captures multi-modality in the\ndistribution. The continuous representation addresses sparsity. Our model ties\ntogether many existing models, linking the linear categorical latent Gaussian\nmodel, the Gaussian process latent variable model, and Gaussian process\nclassification. We derive inference for our model based on recent developments\nin sampling based variational inference. We show empirically that the model\noutperforms its linear and discrete counterparts in imputation tasks of sparse\ndata.","url_abs":"http://arxiv.org/abs/1503.02182v1","url_pdf":"http://arxiv.org/pdf/1503.02182v1.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":"latent-gaussian-processes-for-distribution","repo_url":"https://github.com/yaringal/CLGP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1503.02182","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}