{"id":"https://openalex.org/W1500351990","doi":null,"title":"Large Graph Construction for Scalable Semi-Supervised Learning","display_name":"Large Graph Construction for Scalable Semi-Supervised Learning","publication_year":2010,"publication_date":"2010-06-21","ids":{"openalex":"https://openalex.org/W1500351990","mag":"1500351990"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.170.2083","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.2083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.icml2010.org/papers/16.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5100431792","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0002-3865-8145","observed_orcids":["https://orcid.org/0000-0002-3865-8145","https://orcid.org/0000-0002-3871-9454","https://orcid.org/0000-0002-7196-0944","https://orcid.org/0009-0007-8564-2393"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Department of Electrical Engineering , Columbia University , New York, NY, 10027 , USA"],"raw_orcid":"https://orcid.org/0000-0002-3865-8145","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering , Columbia University , New York, NY, 10027 , USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101252047","display_name":"Junfeng He","orcid":"https://orcid.org/0009-0004-5465-5659","observed_orcids":["https://orcid.org/0009-0004-5465-5659"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junfeng He","raw_affiliation_strings":["Department of Electrical Engineering , Columbia University , New York, NY, 10027 , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering , Columbia University , New York, NY, 10027 , USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037340457","display_name":"Shih\u2010Fu Chang","orcid":"https://orcid.org/0000-0003-1444-1205","observed_orcids":["https://orcid.org/0000-0003-1444-1205"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shih\u2010Fu Chang","raw_affiliation_strings":["Department of Electrical Engineering , Columbia University , New York, NY, 10027 , USA"],"raw_orcid":"https://orcid.org/0000-0003-1444-1205","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering , Columbia University , New York, NY, 10027 , USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":19.3293,"has_fulltext":false,"cited_by_count":557,"citation_normalized_percentile":{"value":0.99552554,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"679","last_page":"686"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9818999767303467,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9818999767303467,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.004800000227987766,"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":0.00430000014603138,"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/graph-construction","display_name":"graph construction","score":0.7310000061988831},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.9710000157356262},{"id":"https://openalex.org/keywords/anchor-points","display_name":"anchor points","score":0.7839999794960022},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"nonparametric regression","score":0.6940000057220459},{"id":"https://openalex.org/keywords/label-prediction","display_name":"label prediction","score":0.6209999918937683},{"id":"https://openalex.org/keywords/graph-laplacian","display_name":"graph Laplacian","score":0.8240000009536743},{"id":"https://openalex.org/keywords/positive-semidefinite-matrices","display_name":"positive semidefinite matrices","score":0.8159999847412109},{"id":"https://openalex.org/keywords/nystrom-approximation","display_name":"Nystr\u00f6m approximation","score":0.7950000166893005},{"id":"https://openalex.org/keywords/scalability","display_name":"scalability","score":0.6790000200271606},{"id":"https://openalex.org/keywords/large-scale-data","display_name":"large-scale data","score":0.527999997138977}],"concepts":[{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.7026946544647217},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6619704365730286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6351004242897034},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5537781715393066},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45747825503349304},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.43153858184814453},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.41882574558258057},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4162730574607849},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.41261959075927734},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3154292702674866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29974135756492615},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27745407819747925},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.170.2083","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.2083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.icml2010.org/papers/16.pdf","raw_type":"text"},{"id":"mag:1500351990","doi":null,"is_oa":false,"landing_page_url":"https://icml.cc/Conferences/2010/papers/16.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1554944419","https://openalex.org/W1578099820","https://openalex.org/W1978259121","https://openalex.org/W1992837555","https://openalex.org/W2053186076","https://openalex.org/W2073614810","https://openalex.org/W2104290444","https://openalex.org/W2107008379","https://openalex.org/W2109405055","https://openalex.org/W2122457239","https://openalex.org/W2127230571","https://openalex.org/W2132146097","https://openalex.org/W2139823104","https://openalex.org/W2142114717","https://openalex.org/W2154455818","https://openalex.org/W2167665791","https://openalex.org/W3141595720"],"related_works":["https://openalex.org/W2903217596","https://openalex.org/W2993369706","https://openalex.org/W2319358180","https://openalex.org/W2982368516","https://openalex.org/W4391128984","https://openalex.org/W4327652721","https://openalex.org/W2479246885","https://openalex.org/W4318979379","https://openalex.org/W2026731079","https://openalex.org/W4405761677"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,43],"address":[4],"the":[5,22,34,62,84,111,128,135],"scalability":[6,133],"issue":[7],"plaguing":[8],"graph-based":[9],"semi-supervised":[10],"learningviaasmallnumberofanchorpointswhich":[11],"adequatelycovertheentirepointcloud.":[12],"Critically,":[13],"these":[14],"anchor":[15,37],"points":[16],"enable":[17],"nonparametric":[18],"regression":[19],"that":[20],"predicts":[21],"label":[23,54],"for":[24],"each":[25],"data":[26,112],"point":[27],"as":[28],"a":[29,47,91,119],"locally":[30],"weighted":[31],"average":[32],"of":[33,65,134],"labels":[35],"on":[36,90,124],"points.":[38],"Becauseconventionalgraphconstructionisinefficient":[39],"in":[40,70,75,115],"large":[41,49,120,125],"scale,":[42],"propose":[44],"to":[45,61,78,100],"construct":[46],"tractable":[48],"graph":[50,72,86,104],"by":[51],"coupling":[52],"anchorbased":[53],"prediction":[55],"and":[56,74,114,132],"adjacency":[57,66,98],"matrix":[58],"design.":[59],"Contrary":[60],"Nystr\u00f6m":[63],"approximation":[64],"matrices":[67,99],"which":[68],"results":[69],"indefinite":[71],"Laplacians":[73],"turn":[76],"leads":[77],"potential":[79],"non-convex":[80],"optimization":[81],"over":[82],"graphs,":[83],"proposed":[85,136],"construction":[87],"approach":[88,107],"based":[89],"unique":[92],"idea":[93],"called":[94],"AnchorGraph":[95],"provides":[96],"nonnegative":[97],"guarantee":[101],"positive":[102],"semidefinite":[103],"Laplacians.":[105],"Our":[106],"scales":[108],"linearly":[109],"with":[110],"size":[113],"practice":[116],"usually":[117],"produces":[118],"sparse":[121],"graph.":[122],"Experiments":[123],"datasets":[126],"demonstrate":[127],"significant":[129],"accuracy":[130],"improvement":[131],"approach.":[137],"1.":[138]},"counts_by_year":[{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":31},{"year":2023,"cited_by_count":30},{"year":2022,"cited_by_count":35},{"year":2021,"cited_by_count":43},{"year":2020,"cited_by_count":37},{"year":2019,"cited_by_count":50},{"year":2018,"cited_by_count":42},{"year":2017,"cited_by_count":52},{"year":2016,"cited_by_count":60},{"year":2015,"cited_by_count":43},{"year":2014,"cited_by_count":46},{"year":2013,"cited_by_count":34},{"year":2012,"cited_by_count":20}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
