{"id":"https://openalex.org/W4391801128","doi":"https://doi.org/10.1145/3654823.3654895","title":"Parameterizations for Gradient-based Markov Chain Monte Carlo on the Stiefel Manifold: A Comparative Study","display_name":"Parameterizations for Gradient-based Markov Chain Monte Carlo on the Stiefel Manifold: A Comparative Study","publication_year":2024,"publication_date":"2024-03-22","ids":{"openalex":"https://openalex.org/W4391801128","doi":"https://doi.org/10.1145/3654823.3654895"},"language":"en","primary_location":{"id":"doi:10.1145/3654823.3654895","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654823.3654895","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2402.07434","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101547868","display_name":"Masahiro Tanaka","orcid":"https://orcid.org/0000-0002-2269-8468"},"institutions":[{"id":"https://openalex.org/I31784960","display_name":"Fukuoka University","ror":"https://ror.org/04nt8b154","country_code":"JP","type":"education","lineage":["https://openalex.org/I31784960"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Masahiro Tanaka","raw_affiliation_strings":["Faculty of Economics, Fukuoka University, Japan"],"raw_orcid":"https://orcid.org/0000-0002-2269-8468","affiliations":[{"raw_affiliation_string":"Faculty of Economics, Fukuoka University, Japan","institution_ids":["https://openalex.org/I31784960"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101547868"],"corresponding_institution_ids":["https://openalex.org/I31784960"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0110537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"397","last_page":"402"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9768000245094299,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9768000245094299,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9596999883651733,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9406999945640564,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stiefel-manifold","display_name":"Stiefel manifold","score":0.7006780505180359},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.693635106086731},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.6660622954368591},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5393824577331543},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.5381985306739807},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.5103070139884949},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5025818347930908},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4827224910259247},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.4425228536128998},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.42756032943725586},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4176221489906311},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.41253942251205444},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3527185022830963},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.33036738634109497},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1756230890750885}],"concepts":[{"id":"https://openalex.org/C612670","wikidata":"https://www.wikidata.org/wiki/Q7616373","display_name":"Stiefel manifold","level":2,"score":0.7006780505180359},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.693635106086731},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.6660622954368591},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5393824577331543},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.5381985306739807},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5103070139884949},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5025818347930908},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4827224910259247},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.4425228536128998},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.42756032943725586},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4176221489906311},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.41253942251205444},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3527185022830963},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.33036738634109497},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1756230890750885},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3654823.3654895","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654823.3654895","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2402.07434","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.07434","pdf_url":"https://arxiv.org/pdf/2402.07434","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2402.07434","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.07434","pdf_url":"https://arxiv.org/pdf/2402.07434","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5211211209","display_name":null,"funder_award_id":"20K22096","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G8859214291","display_name":"Development of Bayesian Matrix Completion Methods and Application to Economic Statistics","funder_award_id":"20K22096","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391801128.pdf","grobid_xml":"https://content.openalex.org/works/W4391801128.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1968480431","https://openalex.org/W2059448777","https://openalex.org/W2084008613","https://openalex.org/W2125027820","https://openalex.org/W2129728285","https://openalex.org/W2141159272","https://openalex.org/W2166137558","https://openalex.org/W2171656257","https://openalex.org/W2411061461","https://openalex.org/W2429877846","https://openalex.org/W2750458177","https://openalex.org/W2963762195","https://openalex.org/W2979722846","https://openalex.org/W2983402365","https://openalex.org/W2988533513","https://openalex.org/W2997591727","https://openalex.org/W3003561462","https://openalex.org/W3022683461","https://openalex.org/W3103120865","https://openalex.org/W3113137221","https://openalex.org/W3120081705","https://openalex.org/W3125057276","https://openalex.org/W3149334336","https://openalex.org/W3181158838","https://openalex.org/W4288701819","https://openalex.org/W4380484875","https://openalex.org/W4389451492"],"related_works":["https://openalex.org/W4309301408","https://openalex.org/W3103377301","https://openalex.org/W2478683457","https://openalex.org/W1489016866","https://openalex.org/W4235385816","https://openalex.org/W4239322872","https://openalex.org/W3022940228","https://openalex.org/W4251525937","https://openalex.org/W4242294605","https://openalex.org/W2067623561"],"abstract_inverted_index":{"Orthogonal":[0],"matrices":[1,20,138],"play":[2],"an":[3],"important":[4],"role":[5],"in":[6,11,35,116,172,184],"probability":[7],"and":[8,24,74,164],"statistics,":[9],"particularly":[10,160],"high-dimensional":[12,163,174],"statistical":[13,33],"models.":[14],"Parameterizing":[15],"these":[16,36,112],"models":[17,37],"using":[18],"orthogonal":[19,82,137,188],"facilitates":[21],"dimension":[22],"reduction":[23],"parameter":[25],"identification.":[26],"However,":[27,107,167],"establishing":[28],"the":[29,77,104,117,132,157,162,179],"theoretical":[30],"validity":[31],"of":[32,52,71,111,131,136],"inference":[34],"from":[38],"a":[39,46,108],"frequentist":[40],"perspective":[41],"is":[42,103,114,156],"challenging,":[43],"leading":[44],"to":[45,55,123],"preference":[47],"for":[48,68,93,146,161,181,187],"Bayesian":[49],"approaches":[50],"because":[51],"their":[53],"ability":[54],"offer":[56],"consistent":[57],"uncertainty":[58],"quantification.":[59],"Markov":[60,97],"chain":[61,98],"Monte":[62,99],"Carlo":[63,100],"methods":[64,186],"are":[65],"commonly":[66],"used":[67],"numerical":[69,129],"approximation":[70],"posterior":[72],"distributions,":[73],"sampling":[75,185],"on":[76],"Stiefel":[78],"manifold,":[79],"which":[80],"comprises":[81],"matrices,":[83],"poses":[84],"significant":[85],"difficulties.":[86],"While":[87],"various":[88],"strategies":[89],"have":[90],"been":[91],"proposed":[92],"this":[94,125],"purpose,":[95],"gradient-based":[96],"with":[101],"parameterizations":[102,113,135,169],"most":[105,158],"efficient.":[106],"comprehensive":[109],"comparison":[110],"lacking":[115],"existing":[118],"literature.":[119],"This":[120],"study":[121],"aims":[122],"address":[124],"gap":[126],"by":[127],"evaluating":[128],"efficiency":[130],"four":[133,147],"alternative":[134],"under":[139],"equivalent":[140],"conditions.":[141],"The":[142,149],"evaluation":[143],"was":[144],"conducted":[145],"problems.":[148,166],"results":[150],"suggest":[151],"that":[152],"polar":[153],"expansion":[154],"parameterization":[155],"efficient,":[159],"complex":[165],"all":[168],"exhibit":[170],"limitations":[171],"significantly":[173],"or":[175],"difficult":[176],"tasks,":[177],"emphasizing":[178],"need":[180],"further":[182],"advancements":[183],"matrices.":[189]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}