{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-8510257","patent":{"patent_number":"US-8510257","title":"Collapsed gibbs sampler for sparse topic models and discrete matrix factorization","assignee":null,"inventors":[],"filing_date":"2010-10-19T00:00:00.000Z","publication_date":"2013-08-13T00:00:00.000Z","cpc_codes":["G06F","G06F","G06F","G06F","G06N"],"num_claims":21,"abstract":"In an inference system for organizing a corpus of objects, feature representations are generated comprising distributions over a set of features corresponding to the objects. A topic model defining a set of topics is inferred by performing latent Dirichlet allocation (LDA) with an Indian Buffet Process (IBP) compound Dirichlet prior probability distribution. The inference is performed using a collapsed Gibbs sampling algorithm by iteratively sampling (1) topic allocation variables of the LDA and (2) binary activation variables of the IBP compound Dirichlet prior. In some embodiments the inference is configured such that each inferred topic model is a clean topic model with topics defined as distributions over sub-sets of the set of features selected by the prior. In some embodiments the inference is configured such that the inferred topic model associates a focused sub-set of the set of topics to each object of the training corpus."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Collapsed gibbs sampler for sparse topic models and discrete matrix factorization","description":"In an inference system for organizing a corpus of objects, feature representations are generated comprising distributions over a set of features corresponding to the objects. A topic model defining a ","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-8510257","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-8510257","citation_suggestion":"Patentable. \"Collapsed gibbs sampler for sparse topic models and discrete matrix factorization\" (US-8510257). https://patentable.app/patents/US-8510257","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-8510257","json":"https://patentable.app/api/llm-context/US-8510257","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T22:14:20.462Z"}