{"id":"https://openalex.org/W1534577160","doi":null,"title":"Bayesian group latent factor analysis with structured sparse priors","display_name":"Bayesian group latent factor analysis with structured sparse priors","publication_year":2014,"publication_date":"2014-11-11","ids":{"openalex":"https://openalex.org/W1534577160","mag":"1534577160"},"language":"en","primary_location":{"id":"mag:1534577160","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1411.2698?context=stat.ML","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/abs/1411.2698?context=stat.ML","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112484713","display_name":"Shiwen Zhao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shiwen Zhao","raw_affiliation_strings":["Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chuan Gao","orcid":"https://orcid.org/0000-0002-1399-8450","observed_orcids":["https://orcid.org/0000-0002-1399-8450"]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuan Gao","raw_affiliation_strings":["Duke University"],"raw_orcid":"https://orcid.org/0000-0002-1399-8450","affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014241799","display_name":"Sayan Mukherjee","orcid":"https://orcid.org/0000-0002-6715-3920","observed_orcids":["https://orcid.org/0000-0002-6715-3920"]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sayan Mukherjee","raw_affiliation_strings":["Duke University"],"raw_orcid":"https://orcid.org/0000-0002-6715-3920","affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065136437","display_name":"Barbara Elizabeth Engelhardt","orcid":"https://orcid.org/0000-0002-6139-7334","observed_orcids":["https://orcid.org/0000-0002-6139-7334"]},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Barbara E. Engelhardt","raw_affiliation_strings":["Princeton University#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-6139-7334","affiliations":[{"raw_affiliation_string":"Princeton University#TAB#","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.43869999051094055,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.43869999051094055,"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/T13141","display_name":"Statistical Methods and Applications","score":0.17800000309944153,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.08749999850988388,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/latent-factor-models","display_name":"latent factor models","score":0.9520000219345093},{"id":"https://openalex.org/keywords/shrinkage","display_name":"shrinkage","score":0.5170000195503235},{"id":"https://openalex.org/keywords/gene-co-expression-network","display_name":"gene co-expression network","score":0.890999972820282},{"id":"https://openalex.org/keywords/genetic-variants","display_name":"genetic variants","score":0.625},{"id":"https://openalex.org/keywords/low-dimensional-projection","display_name":"low-dimensional projection","score":0.5979999899864197}],"concepts":[{"id":"https://openalex.org/C10879293","wikidata":"https://www.wikidata.org/wiki/Q726474","display_name":"Factor analysis","level":2,"score":0.7578027844429016},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6593722105026245},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.602066159248352},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5940812826156616},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.5132915377616882},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5117875933647156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47411373257637024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45484694838523865},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.445298433303833},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4281831979751587},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.42377936840057373},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41960546374320984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3103368878364563},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3047829866409302}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:1534577160","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1411.2698?context=stat.ML","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.760.7351","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.760.7351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1411.2698.pdf","raw_type":"text"}],"best_oa_location":{"id":"mag:1534577160","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1411.2698?context=stat.ML","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":75,"referenced_works":["https://openalex.org/W42729272","https://openalex.org/W114565888","https://openalex.org/W1494413412","https://openalex.org/W1545687120","https://openalex.org/W1567512734","https://openalex.org/W1575567117","https://openalex.org/W1592941960","https://openalex.org/W1604799237","https://openalex.org/W1625086225","https://openalex.org/W1648445109","https://openalex.org/W1814082361","https://openalex.org/W1880944740","https://openalex.org/W1974611538","https://openalex.org/W1975900269","https://openalex.org/W1976365540","https://openalex.org/W1982652137","https://openalex.org/W1985629708","https://openalex.org/W1994309289","https://openalex.org/W1998966727","https://openalex.org/W1999974018","https://openalex.org/W2008929650","https://openalex.org/W2020082133","https://openalex.org/W2022821295","https://openalex.org/W2025341678","https://openalex.org/W2029721016","https://openalex.org/W2030951629","https://openalex.org/W2033765726","https://openalex.org/W2035751541","https://openalex.org/W2040854171","https://openalex.org/W2041779820","https://openalex.org/W2047028564","https://openalex.org/W2049633694","https://openalex.org/W2053609837","https://openalex.org/W2054110507","https://openalex.org/W2056165766","https://openalex.org/W2071128523","https://openalex.org/W2072460814","https://openalex.org/W2078803352","https://openalex.org/W2085459418","https://openalex.org/W2086953401","https://openalex.org/W2087101057","https://openalex.org/W2092334563","https://openalex.org/W2096192437","https://openalex.org/W2098143048","https://openalex.org/W2098290597","https://openalex.org/W2099741732","https://openalex.org/W2102701235","https://openalex.org/W2103633133","https://openalex.org/W2108687351","https://openalex.org/W2114169935","https://openalex.org/W2117988331","https://openalex.org/W2117994680","https://openalex.org/W2122825543","https://openalex.org/W2125027820","https://openalex.org/W2125290066","https://openalex.org/W2126497681","https://openalex.org/W2130336429","https://openalex.org/W2135046866","https://openalex.org/W2135194391","https://openalex.org/W2138019504","https://openalex.org/W2144903813","https://openalex.org/W2146610201","https://openalex.org/W2151544098","https://openalex.org/W2155085787","https://openalex.org/W2156872152","https://openalex.org/W2157752701","https://openalex.org/W2157801062","https://openalex.org/W2158217645","https://openalex.org/W2162315509","https://openalex.org/W2163948609","https://openalex.org/W2168796889","https://openalex.org/W2244252827","https://openalex.org/W2493031458","https://openalex.org/W2497431972","https://openalex.org/W2509600984"],"related_works":["https://openalex.org/W2339443313","https://openalex.org/W2557380354","https://openalex.org/W4391296475","https://openalex.org/W4403189998","https://openalex.org/W1589435094","https://openalex.org/W1604799237","https://openalex.org/W1972822237","https://openalex.org/W7100484841","https://openalex.org/W7095309345","https://openalex.org/W2029721016"],"abstract_inverted_index":{"Latent":[0],"factor":[1,30,37,55,71,131,219],"models":[2],"are":[3],"the":[4,36,53,96,100,111,141,181,191,199],"canonical":[5,90],"statistical":[6],"tool":[7],"for":[8,15,150],"exploratory":[9],"analyses":[10],"of":[11,110,143,161,167,174,177,184,190,198],"low-dimensional":[12,209],"linear":[13,196],"structure":[14,106,127],"an":[16],"observation":[17,42,112],"matrix":[18],"with":[19,75,125,213],"p":[20],"features":[21],"across":[22,107],"n":[23],"samples.":[24],"We":[25,94,118,138,179],"develop":[26],"a":[27,47,64,70,175,202],"Bayesian":[28,49,89],"group":[29],"analysis":[31,92,220],"(BGFA)":[32],"model":[33,38,45,86,102,121],"that":[34,78,170],"extends":[35],"to":[39,88,103,146,159,186,194],"multiple":[40,188],"coupled":[41],"matrices.":[43],"Our":[44],"puts":[46],"structured":[48],"hierarchical":[50],"prior":[51],"on":[52,122],"joint":[54],"loading":[56],"matrix,":[57],"which":[58],"achieves":[59],"shrinkage":[60,97],"effect":[61],"at":[62],"both":[63],"local":[65],"level":[66,72],"(element-wise":[67],"shrinkage)":[68,74],"and":[69,128,164,207],"(column-wise":[73],"non-parametric":[76],"behavior":[77,98],"removes":[79],"unnecessary":[80],"factors.":[81],"With":[82],"two":[83,147,162],"observations,":[84],"our":[85,120],"reduces":[87],"correlation":[91],"(BCCA).":[93],"exploit":[95],"in":[99],"BGFA":[101,145,185],"recover":[104],"covariance":[105],"all":[108],"subsets":[109],"matrices":[113],"where":[114],"this":[115],"signal":[116],"exists.":[117],"validate":[119],"simulated":[123],"data":[124,200],"substantial":[126],"compare":[129],"recovered":[130],"loadings":[132],"against":[133],"results":[134,142],"from":[135,217],"related":[136],"methods.":[137],"then":[139],"show":[140],"applying":[144],"genomics":[148],"studies":[149],"different":[151],"analytic":[152],"aims:":[153],"identifying":[154],"gene":[155],"co-expression":[156],"networks":[157],"specific":[158],"one":[160],"conditions,":[163],"recovering":[165],"sets":[166],"genetic":[168],"variants":[169],"jointly":[171],"regulate":[172],"transcription":[173],"collection":[176],"genes.":[178],"illustrate":[180],"unique":[182],"ability":[183],"use":[187],"observations":[189],"same":[192],"samples":[193],"guide":[195],"projection":[197],"onto":[201],"latent":[203],"space,":[204],"producing":[205],"meaningful":[206],"robust":[208],"representations,":[210],"as":[211],"compared":[212],"\u2018unsupervised":[214],"\u2019":[215],"projections":[216],"traditional":[218],"or":[221],"principal":[222],"components":[223],"analysis.":[224]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}