{"id":"https://openalex.org/W7131421931","doi":"https://doi.org/10.1109/icdm65498.2025.00065","title":"A Theoretical Framework for Self-Supervised Contrastive Learning for Continuous Dependent Data","display_name":"A Theoretical Framework for Self-Supervised Contrastive Learning for Continuous Dependent Data","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7131421931","doi":"https://doi.org/10.1109/icdm65498.2025.00065"},"language":null,"primary_location":{"id":"doi:10.1109/icdm65498.2025.00065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00065","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5126825421","display_name":"Alexander Marusov","orcid":null},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Alexander Marusov","raw_affiliation_strings":["Applied AI Center Skoltech,Moscow,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied AI Center Skoltech,Moscow,Russia","institution_ids":["https://openalex.org/I125989756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107179521","display_name":"Aleksandr Yugay","orcid":null},"institutions":[{"id":"https://openalex.org/I9115533","display_name":"Moscow Polytechnic University","ror":"https://ror.org/03paz2a60","country_code":"RU","type":"education","lineage":["https://openalex.org/I9115533"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Aleksandr Yugay","raw_affiliation_strings":["MIPT,Applied AI Center Skoltech,Moscow,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIPT,Applied AI Center Skoltech,Moscow,Russia","institution_ids":["https://openalex.org/I9115533"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126784155","display_name":"Alexey Zaytsev","orcid":null},"institutions":[{"id":"https://openalex.org/I2970213756","display_name":"Alltech (United States)","ror":"https://ror.org/01gh6ja41","country_code":"US","type":"company","lineage":["https://openalex.org/I2970213756"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexey Zaytsev","raw_affiliation_strings":["Risk Management Sber,Applied AI Center Skoltech,Moscow,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Risk Management Sber,Applied AI Center Skoltech,Moscow,Russia","institution_ids":["https://openalex.org/I2970213756"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.673,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.89535233,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"575","last_page":"584"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.265500009059906,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.265500009059906,"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":0.23199999332427979,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.06210000067949295,"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/closeness","display_name":"Closeness","score":0.7542999982833862},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5947999954223633},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5825999975204468},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.5249999761581421},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4092000126838684},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.39089998602867126},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.33820000290870667},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.3319000005722046},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.32429999113082886}],"concepts":[{"id":"https://openalex.org/C2779545769","wikidata":"https://www.wikidata.org/wiki/Q5135364","display_name":"Closeness","level":2,"score":0.7542999982833862},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7020999789237976},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5947999954223633},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5825999975204468},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5794000029563904},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.5249999761581421},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.39089998602867126},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35690000653266907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3433000147342682},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28029999136924744},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm65498.2025.00065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00065","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2086079757","https://openalex.org/W2163922914","https://openalex.org/W2725897987","https://openalex.org/W2988244882","https://openalex.org/W3003709258","https://openalex.org/W3080427797","https://openalex.org/W3145450063","https://openalex.org/W3159481202","https://openalex.org/W3199148273","https://openalex.org/W4281673063","https://openalex.org/W4283752670","https://openalex.org/W4290988258","https://openalex.org/W4302763825","https://openalex.org/W4321461227","https://openalex.org/W4377000545","https://openalex.org/W4387123372","https://openalex.org/W4390190617","https://openalex.org/W4393153153","https://openalex.org/W4395464266","https://openalex.org/W4400110548","https://openalex.org/W4402733566","https://openalex.org/W4407831855","https://openalex.org/W4407832074","https://openalex.org/W4407832167","https://openalex.org/W4407832263","https://openalex.org/W4409301292","https://openalex.org/W4409365538"],"related_works":[],"abstract_inverted_index":{"Self-supervised":[0],"learning":[1,10],"(SSL)":[2],"has":[3],"emerged":[4],"as":[5,26],"a":[6,56,198],"powerful":[7],"approach":[8,143],"to":[9,22,64,73,77],"representations,":[11],"particularly":[12],"in":[13,139,161],"the":[14,70,105,135,140,151,170,186],"field":[15],"of":[16,113,153,179],"computer":[17],"vision.":[18],"However,":[19],"its":[20],"application":[21],"dependent":[23,49,66,148],"data,":[24,67,141,149],"such":[25],"temporal":[27,129],"and":[28,94,130,173,181],"spatio-temporal":[29,131,163,193],"domains,":[30],"remains":[31],"underexplored.":[32],"Besides,":[33],"traditional":[34],"contrastive":[35,61],"SSL":[36,62,160],"methods":[37],"often":[38],"assume":[39],"semantic":[40],"independence":[41],"between":[42,90,115],"samples,":[43,116],"which":[44,68,190],"does":[45],"not":[46],"hold":[47],"for":[48,60,104,147,159],"data":[50],"exhibiting":[51],"complex":[52,192],"correlations.":[53],"We":[54,122],"propose":[55,83],"novel":[57],"theoretical":[58],"framework":[59],"tailored":[63],"continuous":[65],"allows":[69],"nearest":[71],"samples":[72],"be":[74],"semantically":[75],"close":[76],"each":[78],"other.":[79],"In":[80],"particular,":[81],"we":[82,99,166],"two":[84],"possible":[85],"ground":[86],"truth":[87],"similarity":[88,107],"measures":[89],"objects":[91],"\u2014":[92],"hard":[93],"soft":[95],"closeness.":[96],"Under":[97],"it,":[98],"derive":[100],"an":[101],"analytical":[102],"form":[103],"estimated":[106],"matrix":[108],"that":[109],"accommodates":[110],"both":[111],"types":[112],"closeness":[114],"thereby":[117],"introducing":[118],"dependency-aware":[119],"loss":[120,157],"functions.":[121],"validate":[123],"our":[124,142,154,195],"approach,":[125],"Dependent":[126],"TS2Vec,":[127],"on":[128,169,185],"downstream":[132],"problems.":[133],"Given":[134],"dependency":[136],"patterns":[137],"presented":[138],"surpasses":[144],"modern":[145],"ones":[146],"highlighting":[150],"effectiveness":[152],"theoretically":[155],"grounded":[156],"functions":[158],"capturing":[162],"dependencies.":[164],"Specifically,":[165],"outperform":[167],"TS2Vec":[168],"standard":[171],"UEA":[172],"UCR":[174],"benchmarks,":[175],"with":[176],"accuracy":[177],"improvements":[178],"4.17%":[180],"2.08%,":[182],"respectively.":[183],"Furthermore,":[184],"drought":[187],"classification":[188],"task,":[189],"involves":[191],"patterns,":[194],"method":[196],"achieves":[197],"7%":[199],"higher":[200],"ROC-AUC":[201],"score.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-26T00:00:00"}
