{"id":"https://openalex.org/W3049472892","doi":"https://doi.org/10.1007/s10994-020-05895-3","title":"Learning representations from dendrograms","display_name":"Learning representations from dendrograms","publication_year":2020,"publication_date":"2020-08-16","ids":{"openalex":"https://openalex.org/W3049472892","doi":"https://doi.org/10.1007/s10994-020-05895-3","mag":"3049472892"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-020-05895-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-020-05895-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-020-05895-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-020-05895-3.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103015876","display_name":"Morteza Haghir Chehreghani","orcid":"https://orcid.org/0000-0002-2912-7422"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]},{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["IR","SE"],"is_corresponding":true,"raw_author_name":"Morteza Haghir Chehreghani","raw_affiliation_strings":["Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran","Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-2912-7422","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049221896","display_name":"Mostafa Haghir Chehreghani","orcid":"https://orcid.org/0000-0003-3436-0541"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]},{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["IR","SE"],"is_corresponding":false,"raw_author_name":"Mostafa Haghir Chehreghani","raw_affiliation_strings":["Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran","Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103015876"],"corresponding_institution_ids":["https://openalex.org/I158248296","https://openalex.org/I66862912"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.8651,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.73120633,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"109","issue":"9-10","first_page":"1779","last_page":"1802"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9828000068664551,"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"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9828000068664551,"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"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9491000175476074,"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"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9391000270843506,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5284528732299805},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5216615200042725},{"id":"https://openalex.org/keywords/dendrogram","display_name":"Dendrogram","score":0.44173651933670044},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35976383090019226},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3491172194480896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3394821882247925},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.12123394012451172}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5284528732299805},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5216615200042725},{"id":"https://openalex.org/C172312944","wikidata":"https://www.wikidata.org/wiki/Q1957903","display_name":"Dendrogram","level":4,"score":0.44173651933670044},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35976383090019226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3491172194480896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3394821882247925},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.12123394012451172},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0},{"id":"https://openalex.org/C81977670","wikidata":"https://www.wikidata.org/wiki/Q585259","display_name":"Genetic diversity","level":3,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s10994-020-05895-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-020-05895-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-020-05895-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1812.09225","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.09225","pdf_url":"https://arxiv.org/pdf/1812.09225","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:research.chalmers.se:518936","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/518936","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null}],"best_oa_location":{"id":"doi:10.1007/s10994-020-05895-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-020-05895-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-020-05895-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G864766425","display_name":null,"funder_award_id":"WASP-AI/MLX 2019","funder_id":"https://openalex.org/F4320322327","funder_display_name":"Knut och Alice Wallenbergs Stiftelse"}],"funders":[{"id":"https://openalex.org/F4320321523","display_name":"Chalmers Tekniska H\u00f6gskola","ror":"https://ror.org/040wg7k59"},{"id":"https://openalex.org/F4320322327","display_name":"Knut och Alice Wallenbergs Stiftelse","ror":"https://ror.org/004hzzk67"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3049472892.pdf","grobid_xml":"https://content.openalex.org/works/W3049472892.grobid-xml"},"referenced_works_count":68,"referenced_works":["https://openalex.org/W108464071","https://openalex.org/W1510073064","https://openalex.org/W1655990431","https://openalex.org/W1963752964","https://openalex.org/W1982444805","https://openalex.org/W1985875030","https://openalex.org/W1986007546","https://openalex.org/W1986175898","https://openalex.org/W2007995029","https://openalex.org/W2016381774","https://openalex.org/W2019854450","https://openalex.org/W2026513874","https://openalex.org/W2049240645","https://openalex.org/W2056930330","https://openalex.org/W2061681393","https://openalex.org/W2064106255","https://openalex.org/W2084642062","https://openalex.org/W2097990715","https://openalex.org/W2098063062","https://openalex.org/W2099097464","https://openalex.org/W2105295920","https://openalex.org/W2113239207","https://openalex.org/W2125464731","https://openalex.org/W2127218421","https://openalex.org/W2132914434","https://openalex.org/W2137813581","https://openalex.org/W2138615112","https://openalex.org/W2140095548","https://openalex.org/W2143654071","https://openalex.org/W2145799156","https://openalex.org/W2148894497","https://openalex.org/W2153315886","https://openalex.org/W2157063016","https://openalex.org/W2162833336","https://openalex.org/W2166319632","https://openalex.org/W2168029744","https://openalex.org/W2170432751","https://openalex.org/W2171903410","https://openalex.org/W2181557923","https://openalex.org/W2229908198","https://openalex.org/W2296594285","https://openalex.org/W2418025139","https://openalex.org/W2461565936","https://openalex.org/W2507846080","https://openalex.org/W2604990564","https://openalex.org/W2605349387","https://openalex.org/W2611775752","https://openalex.org/W2732000743","https://openalex.org/W2768467070","https://openalex.org/W2773737997","https://openalex.org/W2774226891","https://openalex.org/W2904886400","https://openalex.org/W2914959486","https://openalex.org/W2919115771","https://openalex.org/W2974587601","https://openalex.org/W2975800720","https://openalex.org/W3045759936","https://openalex.org/W3101749733","https://openalex.org/W3152330821","https://openalex.org/W3163397523","https://openalex.org/W4229753684","https://openalex.org/W4229926969","https://openalex.org/W4235169531","https://openalex.org/W6662738692","https://openalex.org/W6664635345","https://openalex.org/W6675024448","https://openalex.org/W6682872970","https://openalex.org/W6717305147"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W2033914206"],"abstract_inverted_index":{"Abstract":[0],"We":[1,48],"propose":[2],"unsupervised":[3],"representation":[4,80,119],"learning":[5,92],"and":[6,34,57,69,117,143,192],"feature":[7],"extraction":[8],"from":[9,62],"dendrograms.":[10,47],"The":[11],"commonly":[12],"used":[13],"Minimax":[14],"distance":[15,36,55,70],"measures":[16,56],"correspond":[17],"to":[18,45,87,94,99,148,176,201],"building":[19],"a":[20,31,35,50,78,139],"dendrogram":[21],"with":[22,26,141],"single":[23],"linkage":[24],"criterion,":[25],"defining":[27],"specific":[28],"forms":[29],"of":[30,65,81,109,124,151,155,164,173,189,198,211],"level":[32,67],"function":[33,37],"over":[38],"that.":[39],"Therefore,":[40],"we":[41,76,105,137,168,185,207],"extend":[42],"this":[43],"method":[44],"arbitrary":[46],"develop":[49],"generalized":[51],"framework":[52],"wherein":[53],"different":[54,63,110,156,159,190],"representations":[58],"can":[59],"be":[60],"inferred":[61,83],"types":[64],"dendrograms,":[66],"functions":[68],"functions.":[71],"Via":[72],"an":[73,170],"appropriate":[74],"embedding,":[75],"compute":[77],"vector-based":[79],"the":[82,101,107,122,128,134,149,152,162,178,182,187,196,203,209],"distances,":[84],"in":[85,114,118,121,161,195],"order":[86],"enable":[88],"many":[89],"numerical":[90,216],"machine":[91],"algorithms":[93],"employ":[95],"such":[96],"distances.":[97],"Then,":[98,167],"address":[100],"model":[102],"selection":[103],"problem,":[104,136],"study":[106],"aggregation":[108],"dendrogram-based":[111],"distances":[112,191],"respectively":[113],"solution":[115],"space":[116,120],"spirit":[123,197],"deep":[125],"representations.":[126],"In":[127,181],"first":[129],"approach,":[130,184],"for":[131,133],"example":[132],"clustering":[135,153,175],"build":[138],"graph":[140],"positive":[142],"negative":[144],"edge":[145],"weights":[146],"according":[147],"consistency":[150],"labels":[154],"objects":[157],"among":[158],"solutions,":[160],"context":[163],"ensemble":[165],"methods.":[166],"use":[169],"efficient":[171],"variant":[172],"correlation":[174],"produce":[177],"final":[179,204],"clusters.":[180],"second":[183],"investigate":[186],"combination":[188],"features":[193],"sequentially":[194],"multi-layered":[199],"architectures":[200],"obtain":[202],"features.":[205],"Finally,":[206],"demonstrate":[208],"effectiveness":[210],"our":[212],"approach":[213],"via":[214],"several":[215],"studies.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
