{"id":"https://openalex.org/W2690504732","doi":"https://doi.org/10.1109/icassp.2017.7952671","title":"The counterintuitive mechanism of graph-based semi-supervised learning in the big data regime","display_name":"The counterintuitive mechanism of graph-based semi-supervised learning in the big data regime","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2690504732","doi":"https://doi.org/10.1109/icassp.2017.7952671","mag":"2690504732"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7952671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-01957754","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031616115","display_name":"Xiaoyi Mai","orcid":null},"institutions":[{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]},{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN","FR"],"is_corresponding":false,"raw_author_name":"Xiaoyi Mai","raw_affiliation_strings":["Beihang University, Beijing, China","CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, France","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021257434","display_name":"Romain Couillet","orcid":"https://orcid.org/0000-0001-5755-2090"},"institutions":[{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Romain Couillet","raw_affiliation_strings":["CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, France","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":null,"first_page":"2821","last_page":"2825"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9962000250816345,"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/T10057","display_name":"Face and Expression Recognition","score":0.9962000250816345,"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/T12676","display_name":"Machine Learning and ELM","score":0.9925000071525574,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9918000102043152,"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/mnist-database","display_name":"MNIST database","score":0.7644712924957275},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6618784666061401},{"id":"https://openalex.org/keywords/counterintuitive","display_name":"Counterintuitive","score":0.6049841046333313},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5875784754753113},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5314266681671143},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.507509171962738},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5069472789764404},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4764726161956787},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.461594820022583},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.45164015889167786},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4293190836906433},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.42531758546829224},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3590465188026428},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3236047029495239},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23169469833374023},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.22130221128463745}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7644712924957275},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6618784666061401},{"id":"https://openalex.org/C101097943","wikidata":"https://www.wikidata.org/wiki/Q5176983","display_name":"Counterintuitive","level":2,"score":0.6049841046333313},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5875784754753113},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5314266681671143},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.507509171962738},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5069472789764404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4764726161956787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.461594820022583},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.45164015889167786},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4293190836906433},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.42531758546829224},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3590465188026428},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3236047029495239},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23169469833374023},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.22130221128463745},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp.2017.7952671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01957754v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01957754","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), 2017, New Orleans, United States. &#x27E8;10.1109/icassp.2017.7952671&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-01957754v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01957754","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), 2017, New Orleans, United States. &#x27E8;10.1109/icassp.2017.7952671&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G115824985","display_name":"Random Matrix Theory for Large Dimensional Graphs","funder_award_id":"ANR-14-CE28-0006","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1497443639","https://openalex.org/W1656240941","https://openalex.org/W1698699930","https://openalex.org/W1991252559","https://openalex.org/W2132914434","https://openalex.org/W2139823104","https://openalex.org/W2152461258","https://openalex.org/W2154455818","https://openalex.org/W2963144092","https://openalex.org/W3099880660","https://openalex.org/W6680434193","https://openalex.org/W6682494755"],"related_works":["https://openalex.org/W2809491669","https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W3005839474","https://openalex.org/W4379988549","https://openalex.org/W2750384547","https://openalex.org/W2139510495","https://openalex.org/W4380078352","https://openalex.org/W3046591097"],"abstract_inverted_index":{"In":[0],"this":[1],"article,":[2],"a":[3,42,55,88],"new":[4],"approach":[5],"is":[6],"proposed":[7],"to":[8,58,81],"study":[9],"the":[10,18,21,33,38,50,60,71,83,91,96,99,103,107,109,114,123],"performance":[11,73,115],"of":[12,23,74,90,95,98],"graph-based":[13],"semi-supervised":[14,75],"learning":[15,76],"methods,":[16],"under":[17],"assumptions":[19],"that":[20,37],"dimension":[22],"data":[24,39],"p":[25],"and":[26,36,94],"their":[27],"number":[28],"n":[29],"grow":[30],"large":[31,51],"at":[32],"same":[34],"rate":[35,86],"arise":[40],"from":[41],"Gaussian":[43,104],"mixture":[44],"model.":[45],"Unlike":[46],"small":[47],"dimensional":[48],"systems,":[49],"dimensions":[52],"allow":[53],"for":[54,106],"Taylor":[56],"expansion":[57],"linearize":[59],"weight":[61],"(or":[62],"kernel)":[63],"matrix":[64],"W,":[65],"thereby":[66],"providing":[67],"in":[68],"closed":[69],"form":[70],"limiting":[72],"algorithms.":[77],"This":[78],"notably":[79],"allows":[80],"predict":[82],"classification":[84],"error":[85],"as":[87],"function":[89],"normalization":[92],"parameters":[93],"choice":[97],"kernel":[100],"function.":[101],"Despite":[102],"assumption":[105],"data,":[108],"theoretical":[110],"findings":[111],"match":[112],"closely":[113],"achieved":[116],"with":[117],"real":[118],"datasets,":[119],"particularly":[120],"here":[121],"on":[122],"popular":[124],"MNIST":[125],"database.":[126]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
