{"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/dirichlet-vmf-mixture-model","title":"Dirichlet-vMF Mixture Model","arxiv_id":"1702.07495","date":"2017-02-24","proceeding":null,"authors":["Shaohua Li"],"abstract":"This document is about the multi-document Von-Mises-Fisher mixture model with\na Dirichlet prior, referred to as VMFMix. VMFMix is analogous to Latent\nDirichlet Allocation (LDA) in that they can capture the co-occurrence patterns\nacorss multiple documents. The difference is that in VMFMix, the topic-word\ndistribution is defined on a continuous n-dimensional hypersphere. Hence VMFMix\nis used to derive topic embeddings, i.e., representative vectors, from multiple\nsets of embedding vectors. An efficient Variational Expectation-Maximization\ninference algorithm is derived. The performance of VMFMix on two document\nclassification tasks is reported, with some preliminary analysis.","url_abs":"http://arxiv.org/abs/1702.07495v1","url_pdf":"http://arxiv.org/pdf/1702.07495v1.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":"dirichlet-vmf-mixture-model","repo_url":"https://github.com/askerlee/vmfmix","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}