{"id":"https://openalex.org/W2387215011","doi":"https://doi.org/10.1145/2939672.2939796","title":"Communication Efficient Distributed Kernel Principal Component Analysis","display_name":"Communication Efficient Distributed Kernel Principal Component Analysis","publication_year":2016,"publication_date":"2016-08-08","ids":{"openalex":"https://openalex.org/W2387215011","doi":"https://doi.org/10.1145/2939672.2939796","mag":"2387215011"},"language":"en","primary_location":{"id":"doi:10.1145/2939672.2939796","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","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/A5113498017","display_name":"Maria Florina Balcan","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Maria Florina Balcan","raw_affiliation_strings":["Carnegie Mellon University, Pittsburg, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburg, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035905957","display_name":"Yingyu Liang","orcid":"https://orcid.org/0000-0002-3644-774X"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]},{"id":"https://openalex.org/I4210094960","display_name":"University Medical Center of Princeton","ror":"https://ror.org/00mbhee72","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210094960"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingyu Liang","raw_affiliation_strings":["Princeton University, Plainsboro, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Princeton University, Plainsboro, NJ, USA","institution_ids":["https://openalex.org/I4210094960","https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102898115","display_name":"Le Song","orcid":"https://orcid.org/0000-0003-0345-9899"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Le Song","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102861589","display_name":"David P. Woodruff","orcid":"https://orcid.org/0000-0002-2158-1380"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Woodruff","raw_affiliation_strings":["IBM Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071912894","display_name":"Bo Xie","orcid":"https://orcid.org/0000-0001-7641-5139"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Xie","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5113498017"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":5.1417,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.95672882,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"725","last_page":"734"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9994000196456909,"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/T10320","display_name":"Neural Networks and Applications","score":0.9994000196456909,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9990000128746033,"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/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.9354632496833801},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7809115648269653},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7466433644294739},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.7123199105262756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5491759777069092},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5341227650642395},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4696352183818817},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.45811691880226135},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4336436986923218},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.400272399187088},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3969022333621979},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3455272316932678},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.16729381680488586},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.147396057844162}],"concepts":[{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.9354632496833801},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7809115648269653},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7466433644294739},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7123199105262756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5491759777069092},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5341227650642395},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4696352183818817},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.45811691880226135},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4336436986923218},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.400272399187088},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3969022333621979},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3455272316932678},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.16729381680488586},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.147396057844162},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2939672.2939796","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3023663833","display_name":null,"funder_award_id":"Google Research Award","funder_id":"https://openalex.org/F4320309327","funder_display_name":"Google"},{"id":"https://openalex.org/G4273037125","display_name":null,"funder_award_id":"IIS-1218749,CCF-0953192,CCF-1101283","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4530304537","display_name":null,"funder_award_id":"Microsoft Faculty Fellowship","funder_id":"https://openalex.org/F4320307764","funder_display_name":"Microsoft"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307764","display_name":"Microsoft","ror":"https://ror.org/00d0nc645"},{"id":"https://openalex.org/F4320309327","display_name":"Google","ror":"https://ror.org/00njsd438"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W814830356","https://openalex.org/W1542652324","https://openalex.org/W1986007546","https://openalex.org/W1998269045","https://openalex.org/W2000042664","https://openalex.org/W2018287013","https://openalex.org/W2044610104","https://openalex.org/W2045390367","https://openalex.org/W2093813380","https://openalex.org/W2095846217","https://openalex.org/W2101043704","https://openalex.org/W2105608756","https://openalex.org/W2121689290","https://openalex.org/W2125621954","https://openalex.org/W2127180992","https://openalex.org/W2128175693","https://openalex.org/W2134342155","https://openalex.org/W2141566892","https://openalex.org/W2144902422","https://openalex.org/W2151683004","https://openalex.org/W2157988812","https://openalex.org/W2167608136","https://openalex.org/W2559655401","https://openalex.org/W2616345629","https://openalex.org/W2962696932","https://openalex.org/W2963879412","https://openalex.org/W2990138404","https://openalex.org/W3103362336","https://openalex.org/W3112410551","https://openalex.org/W3119651796","https://openalex.org/W3120740533","https://openalex.org/W6681330998"],"related_works":["https://openalex.org/W2379488555","https://openalex.org/W2753886092","https://openalex.org/W2152632846","https://openalex.org/W1992961908","https://openalex.org/W2014683590","https://openalex.org/W2944973397","https://openalex.org/W2398887903","https://openalex.org/W2127229869","https://openalex.org/W2222829538","https://openalex.org/W2004465977"],"abstract_inverted_index":{"Kernel":[0],"Principal":[1],"Component":[2],"Analysis":[3],"(KPCA)":[4],"is":[5],"a":[6,21,30,44,63],"key":[7],"machine":[8],"learning":[9],"algorithm":[10],"for":[11],"extracting":[12],"nonlinear":[13],"features":[14],"from":[15],"data.":[16],"In":[17],"the":[18,59],"presence":[19],"of":[20,24,40],"large":[22],"volume":[23],"high":[25],"dimensional":[26],"data":[27,42,46],"collected":[28],"in":[29,62,75],"distributed":[31,64],"fashion,":[32],"it":[33],"becomes":[34],"very":[35],"costly":[36],"to":[37,43],"communicate":[38],"all":[39],"this":[41],"single":[45],"center":[47],"and":[48,65,72],"then":[49],"perform":[50,55],"kernel":[51,56],"PCA.":[52],"Can":[53],"we":[54],"PCA":[57],"on":[58],"entire":[60],"dataset":[61],"communication":[66],"efficient":[67],"fashion":[68],"while":[69],"maintaining":[70],"provable":[71],"strong":[73],"guarantees":[74],"solution":[76],"quality?":[77]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}