{"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/causal-patterns-extraction-of-multiple-causal","title":"Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation Analysis","arxiv_id":"1712.04221","date":"2017-12-12","proceeding":null,"authors":["Hiroki Mori","Keisuke Kawano","Hiroki Yokoyama"],"abstract":"In this paper, we propose a mixture of probabilistic partial canonical\ncorrelation analysis (MPPCCA) that extracts the Causal Patterns from two\nmultivariate time series. Causal patterns refer to the signal patterns within\ninteractions of two elements having multiple types of mutually causal\nrelationships, rather than a mixture of simultaneous correlations or the\nabsence of presence of a causal relationship between the elements. In\nmultivariate statistics, partial canonical correlation analysis (PCCA)\nevaluates the correlation between two multivariates after subtracting the\neffect of the third multivariate. PCCA can calculate the Granger Causal- ity\nIndex (which tests whether a time-series can be predicted from an- other\ntime-series), but is not applicable to data containing multiple partial\ncanonical correlations. After introducing the MPPCCA, we propose an\nexpectation-maxmization (EM) algorithm that estimates the parameters and latent\nvariables of the MPPCCA. The MPPCCA is expected to ex- tract multiple partial\ncanonical correlations from data series without any supervised signals to split\nthe data as clusters. The method was then eval- uated in synthetic data\nexperiments. In the synthetic dataset, our method estimated the multiple\npartial canonical correlations more accurately than the existing method. To\ndetermine the types of patterns detectable by the method, experiments were also\nconducted on real datasets. The method estimated the communication patterns In\nmotion-capture data. The MP- PCCA is applicable to various type of signals such\nas brain signals, human communication and nonlinear complex multibody systems.","url_abs":"http://arxiv.org/abs/1712.04221v1","url_pdf":"http://arxiv.org/pdf/1712.04221v1.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":"causal-patterns-extraction-of-multiple-causal","repo_url":"https://github.com/kskkwn/mppcca","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}