{"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/tensormachine-probabilistic-boolean-tensor","title":"TensOrMachine: Probabilistic Boolean Tensor Decomposition","arxiv_id":"1805.04582","date":"2018-05-11","proceeding":null,"authors":["Tammo Rukat","Chris C. Holmes","Christopher Yau"],"abstract":"Boolean tensor decomposition approximates data of multi-way binary\nrelationships as product of interpretable low-rank binary factors, following\nthe rules of Boolean algebra. Here, we present its first probabilistic\ntreatment. We facilitate scalable sampling-based posterior inference by\nexploitation of the combinatorial structure of the factor conditionals. Maximum\na posteriori decompositions feature higher accuracies than existing techniques\nthroughout a wide range of simulated conditions. Moreover, the probabilistic\napproach facilitates the treatment of missing data and enables model selection\nwith much greater accuracy. We investigate three real-world data-sets. First,\ntemporal interaction networks in a hospital ward and behavioural data of\nuniversity students demonstrate the inference of instructive latent patterns.\nNext, we decompose a tensor with more than 10 billion data points, indicating\nrelations of gene expression in cancer patients. Not only does this demonstrate\nscalability, it also provides an entirely novel perspective on relational\nproperties of continuous data and, in the present example, on the molecular\nheterogeneity of cancer. Our implementation is available on GitHub:\nhttps://github.com/TammoR/LogicalFactorisationMachines.","url_abs":"http://arxiv.org/abs/1805.04582v1","url_pdf":"http://arxiv.org/pdf/1805.04582v1.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":"tensormachine-probabilistic-boolean-tensor","repo_url":"https://github.com/TammoR/LogicalFactorisationMachines","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"tensor-decomposition","task_name":"Tensor Decomposition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}