{"id":"https://openalex.org/W2506591631","doi":"https://doi.org/10.1109/taffc.2016.2592918","title":"Modeling Multiple Time Series Annotations as Noisy Distortions of the Ground Truth: An Expectation-Maximization Approach","display_name":"Modeling Multiple Time Series Annotations as Noisy Distortions of the Ground Truth: An Expectation-Maximization Approach","publication_year":2016,"publication_date":"2016-07-19","ids":{"openalex":"https://openalex.org/W2506591631","doi":"https://doi.org/10.1109/taffc.2016.2592918","mag":"2506591631","pmid":"https://pubmed.ncbi.nlm.nih.gov/29644011"},"language":"en","primary_location":{"id":"doi:10.1109/taffc.2016.2592918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2016.2592918","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075937808","display_name":"Rahul Gupta","orcid":"https://orcid.org/0000-0002-9277-3718"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Rahul Gupta","raw_affiliation_strings":["Department of Electrical Engineering, University of Southern California, Los Angeles, California"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Southern California, Los Angeles, California","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015927589","display_name":"Kartik Audhkhasi","orcid":"https://orcid.org/0000-0002-2340-1144"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kartik Audhkhasi","raw_affiliation_strings":["Watson Group, International Business Machines, Yorktown Heights, New York"],"affiliations":[{"raw_affiliation_string":"Watson Group, International Business Machines, Yorktown Heights, New York","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038809303","display_name":"Zach Jacokes","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zach Jacokes","raw_affiliation_strings":["School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia"],"affiliations":[{"raw_affiliation_string":"School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085265585","display_name":"Agata Rozga","orcid":"https://orcid.org/0000-0002-5558-9786"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Agata Rozga","raw_affiliation_strings":["School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia"],"affiliations":[{"raw_affiliation_string":"School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010028928","display_name":"Shrikanth Narayanan","orcid":"https://orcid.org/0000-0002-1052-6204"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shrikanth Narayanan","raw_affiliation_strings":["Department of Electrical Engineering, University of Southern California, Los Angeles, California"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Southern California, Los Angeles, California","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5075937808"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":3.0606,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.92384475,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"9","issue":"1","first_page":"76","last_page":"89"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9728999733924866,"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"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9682000279426575,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/ground-truth","display_name":"Ground truth","score":0.8963626623153687},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7870832681655884},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5950270295143127},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5704883337020874},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5515902042388916},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.5381311774253845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5260239243507385},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4515995383262634},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38877642154693604},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3876887559890747},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11521604657173157}],"concepts":[{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.8963626623153687},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7870832681655884},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5950270295143127},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5704883337020874},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5515902042388916},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.5381311774253845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5260239243507385},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4515995383262634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38877642154693604},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3876887559890747},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11521604657173157},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/taffc.2016.2592918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2016.2592918","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-article"},{"id":"pmid:29644011","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29644011","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on affective computing","raw_type":null},{"id":"pmh:oai:europepmc.org:4798714","is_oa":false,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5891169","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308668","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W9014458","https://openalex.org/W47232516","https://openalex.org/W107619411","https://openalex.org/W193654215","https://openalex.org/W1235794088","https://openalex.org/W1511986666","https://openalex.org/W1521990686","https://openalex.org/W1565072465","https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1736339626","https://openalex.org/W1785074626","https://openalex.org/W1830095924","https://openalex.org/W1884430278","https://openalex.org/W1923565741","https://openalex.org/W1968020335","https://openalex.org/W1973031849","https://openalex.org/W1976066595","https://openalex.org/W1990005915","https://openalex.org/W1999377414","https://openalex.org/W2016038247","https://openalex.org/W2047194288","https://openalex.org/W2049633694","https://openalex.org/W2056403322","https://openalex.org/W2059413283","https://openalex.org/W2060774914","https://openalex.org/W2062733121","https://openalex.org/W2084962682","https://openalex.org/W2085536277","https://openalex.org/W2095540482","https://openalex.org/W2100569924","https://openalex.org/W2103139809","https://openalex.org/W2105268242","https://openalex.org/W2114473842","https://openalex.org/W2124584309","https://openalex.org/W2129479650","https://openalex.org/W2134305421","https://openalex.org/W2136879928","https://openalex.org/W2147272821","https://openalex.org/W2149273804","https://openalex.org/W2154096410","https://openalex.org/W2157653492","https://openalex.org/W2160814043","https://openalex.org/W2163352848","https://openalex.org/W2293617031","https://openalex.org/W2295652995","https://openalex.org/W2396718206","https://openalex.org/W2553700369","https://openalex.org/W2570840460","https://openalex.org/W2791102794","https://openalex.org/W2796901959","https://openalex.org/W3122558716","https://openalex.org/W4210371587","https://openalex.org/W4214585779","https://openalex.org/W4237558092","https://openalex.org/W4243716403","https://openalex.org/W4285719527","https://openalex.org/W6628190690","https://openalex.org/W6633791193","https://openalex.org/W6679959949","https://openalex.org/W6680408311","https://openalex.org/W6682171051","https://openalex.org/W6682471470","https://openalex.org/W6697708723","https://openalex.org/W6712376469","https://openalex.org/W6929385289","https://openalex.org/W7073876970"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W4388145910","https://openalex.org/W1976205134","https://openalex.org/W2381570729","https://openalex.org/W4248336175","https://openalex.org/W3009369890","https://openalex.org/W2031260042","https://openalex.org/W2391445434","https://openalex.org/W4312490297","https://openalex.org/W2535204567"],"abstract_inverted_index":{"Studies":[0],"of":[1,17,49,89,106,145,169],"time-continuous":[2],"human":[3],"behavioral":[4],"phenomena":[5],"often":[6,22],"rely":[7],"on":[8,86,129],"ratings":[9,103,144,170],"from":[10,92,171],"multiple":[11,63],"annotators.":[12],"Since":[13],"the":[14,18,24,50,73,83,93,99,107,119,146,153,160,195],"ground":[15,74,84,108,161],"truth":[16,75,85,162],"target":[19],"construct":[20],"is":[21,27,178],"latent,":[23],"standard":[25],"practice":[26],"to":[28,40,141,165,180],"use":[29],"ad-hoc":[30],"metrics":[31,43],"(such":[32],"as":[33,104],"averaging":[34],"annotator":[35,78,112,177],"ratings).":[36],"Despite":[37],"being":[38],"easy":[39],"compute,":[41],"such":[42],"may":[44],"not":[45],"provide":[46,101],"accurate":[47],"representations":[48],"underlying":[51],"construct.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56],"present":[57],"a":[58,68,87,130,136,139,182],"novel":[59],"method":[60],"for":[61],"modeling":[62,77],"time":[64,184],"series":[65],"annotations":[66,190],"over":[67],"continuous":[69],"variable":[70],"that":[71,98],"computes":[72],"by":[76],"specific":[79,114],"distortions.":[80],"We":[81,117,149],"condition":[82],"set":[88],"features":[90],"extracted":[91],"data":[94],"and":[95,126,138,151,188],"further":[96],"assume":[97],"annotators":[100,173],"their":[102,189],"modification":[105],"truth,":[109],"with":[110],"each":[111,176],"having":[113],"distortion":[115],"tendencies.":[116],"train":[118],"model":[120,154],"using":[121],"an":[122],"Expectation-Maximization":[123],"based":[124],"algorithm":[125],"evaluate":[127],"it":[128],"study":[131],"involving":[132],"natural":[133],"interaction":[134],"between":[135],"child":[137],"psychologist,":[140],"predict":[142],"confidence":[143],"children's":[147],"smiles.":[148],"compare":[150],"analyze":[152],"against":[155],"two":[156],"baselines":[157],"where:":[158],"(i)":[159],"in":[163,186],"considered":[164],"be":[166],"framewise":[167,196],"mean":[168],"various":[172],"and,":[174],"(ii)":[175],"assumed":[179],"bear":[181],"distinct":[183],"delay":[185],"annotation":[187],"are":[191],"aligned":[192],"before":[193],"computing":[194],"mean.":[197]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
