{"id":"https://openalex.org/W7134031872","doi":"https://doi.org/10.48550/arxiv.2603.04458","title":"Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering","display_name":"Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7134031872","doi":"https://doi.org/10.48550/arxiv.2603.04458"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.04458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04458","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.04458","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128230415","display_name":"Yiqun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yiqun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115028400","display_name":"Mingjie Zhao","orcid":"https://orcid.org/0009-0009-5517-4845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Mingjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128243339","display_name":"Yizhou Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yizhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128271796","display_name":"Yang Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128260852","display_name":"Yiu-ming Cheung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheung, Yiu-ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.5228000283241272,"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"}},"topics":[{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.5228000283241272,"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/T10057","display_name":"Face and Expression Recognition","score":0.18160000443458557,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.07680000364780426,"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/categorical-variable","display_name":"Categorical variable","score":0.7962999939918518},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7063999772071838},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5795000195503235},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5587999820709229},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4860999882221222},{"id":"https://openalex.org/keywords/metric-space","display_name":"Metric space","score":0.39809998869895935},{"id":"https://openalex.org/keywords/connection","display_name":"Connection (principal bundle)","score":0.3896999955177307},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.3515999913215637}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7962999939918518},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7063999772071838},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5795000195503235},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4860999882221222},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47929999232292175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47920000553131104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4456999897956848},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4300000071525574},{"id":"https://openalex.org/C198043062","wikidata":"https://www.wikidata.org/wiki/Q180953","display_name":"Metric space","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C149073432","wikidata":"https://www.wikidata.org/wiki/Q4960382","display_name":"Bregman divergence","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.337799996137619},{"id":"https://openalex.org/C2639959","wikidata":"https://www.wikidata.org/wiki/Q1344778","display_name":"Distance measures","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3012000024318695},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.29510000348091125},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.2752000093460083},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.27239999175071716},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.04458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04458","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.04458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04458","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Datasets":[0],"composed":[1],"of":[2,62,109,182,215,225],"numerical":[3,20,78],"and":[4,30,70,79,111,118,140,194,206,227],"categorical":[5,41,80,200],"attributes":[6,21,81,110,131],"(also":[7],"called":[8],"mixed":[9,93],"data":[10,94],"hereinafter)":[11],"are":[12,44],"common":[13],"in":[14,36,51,173,223],"real":[15],"clustering":[16,145,153],"tasks.":[17,154],"Differing":[18,155],"from":[19,156],"that":[22,160],"indicate":[23],"tendencies":[24],"between":[25],"two":[26,58],"concepts":[27,46],"(e.g.,":[28,47],"high":[29],"low":[31],"temperature)":[32],"with":[33,144],"their":[34,97],"values":[35,43,181],"well-defined":[37],"Euclidean":[38],"distance":[39,137,164,197],"space,":[40],"attribute":[42,168,184],"different":[45,48,60,152,212],"occupations)":[49],"embedded":[50],"an":[52,65],"implicit":[53],"space.":[54],"Simultaneously":[55],"exploiting":[56],"these":[57],"very":[59],"types":[61],"information":[63],"is":[64,203],"unavoidable":[66],"but":[67],"challenging":[68],"problem,":[69],"most":[71,157],"advanced":[72],"attempts":[73],"either":[74],"encode":[75],"the":[76,105,142,149,180,196],"heterogeneous":[77,130],"into":[82,132,185],"one":[83],"type,":[84],"or":[85,166],"define":[86],"a":[87,113,133,174],"unified":[88,186],"metric":[89,138,150,198],"for":[90,92,124,136,199],"them":[91],"clustering,":[95],"leaving":[96],"inherent":[98],"connection":[99,106],"unrevealed.":[100],"This":[101],"paper,":[102],"therefore,":[103],"studies":[104],"among":[107],"any-type":[108],"proposes":[112],"novel":[114],"Heterogeneous":[115],"Attribute":[116],"Reconstruction":[117],"Representation":[119],"(HARR)":[120],"learning":[121,143],"paradigm":[122,128],"accordingly":[123],"cluster":[125],"analysis.":[126],"The":[127],"transforms":[129],"homogeneous":[134],"status":[135],"learning,":[139],"integrates":[141],"to":[146,151,170,178,190,211],"automatically":[147],"adapt":[148],"existing":[158],"works":[159],"directly":[161],"adopt":[162],"defined":[163],"metrics":[165],"learn":[167,195],"weights":[169],"search":[171],"clusters":[172,216],"subspace.":[175],"We":[176],"propose":[177],"project":[179],"each":[183],"learnable":[187],"multiple":[188],"spaces":[189],"more":[191,208],"finely":[192],"represent":[193],"data.":[201],"HARR":[202],"parameter-free,":[204],"convergence-guaranteed,":[205],"can":[207],"effectively":[209],"self-adapt":[210],"sought":[213],"number":[214],"$k$.":[217],"Extensive":[218],"experiments":[219],"illustrate":[220],"its":[221],"superiority":[222],"terms":[224],"accuracy":[226],"efficiency.":[228]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-07T00:00:00"}