{"id":"https://openalex.org/W2293711698","doi":"https://doi.org/10.1109/tnnls.2018.2838140","title":"Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach","display_name":"Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach","publication_year":2018,"publication_date":"2018-06-21","ids":{"openalex":"https://openalex.org/W2293711698","doi":"https://doi.org/10.1109/tnnls.2018.2838140","mag":"2293711698","pmid":"https://pubmed.ncbi.nlm.nih.gov/29994133"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2018.2838140","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2018.2838140","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5069308334","display_name":"Liang Lan","orcid":"https://orcid.org/0000-0002-0427-977X"},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Liang Lan","raw_affiliation_strings":["Department of Computer Science, Hong Kong Baptist University, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-0427-977X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Hong Kong Baptist University, Hong Kong","institution_ids":["https://openalex.org/I141568987"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100697510","display_name":"Zhuang Wang","orcid":"https://orcid.org/0000-0002-1614-0601"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]},{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuang Wang","raw_affiliation_strings":["Facebook, Menlo Park, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210114444","https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024663093","display_name":"Shandian Zhe","orcid":"https://orcid.org/0000-0003-0316-9875"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shandian Zhe","raw_affiliation_strings":["School of Computing, University of Utah, Salt Lake City, UT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, University of Utah, Salt Lake City, UT, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037724644","display_name":"Wei Cheng","orcid":"https://orcid.org/0000-0001-5456-626X"},"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Cheng","raw_affiliation_strings":["NEC Laboratories America, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0001-5456-626X","affiliations":[{"raw_affiliation_string":"NEC Laboratories America, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100712167","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0001-9548-0411"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Wang","raw_affiliation_strings":["School of Computer Science and Software Engineering, East China Normal University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Software Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100702869","display_name":"Kai Zhang","orcid":"https://orcid.org/0000-0002-9692-4333"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai Zhang","raw_affiliation_strings":["Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"],"raw_orcid":"https://orcid.org/0000-0002-9692-4333","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.2239,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.95153736,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"30","issue":"2","first_page":"369","last_page":"378"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9987999796867371,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9959999918937683,"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/scaling","display_name":"Scaling","score":0.7619284391403198},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.642901599407196},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.623142659664154},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6157374382019043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5234229564666748},{"id":"https://openalex.org/keywords/linearization","display_name":"Linearization","score":0.5006313323974609},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44575971364974976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44028571248054504},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33671581745147705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33531811833381653},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3251042366027832},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.12028282880783081},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.11160668730735779},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08249148726463318},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.04878678917884827}],"concepts":[{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.7619284391403198},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.642901599407196},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.623142659664154},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6157374382019043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5234229564666748},{"id":"https://openalex.org/C11210021","wikidata":"https://www.wikidata.org/wiki/Q1520713","display_name":"Linearization","level":3,"score":0.5006313323974609},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44575971364974976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44028571248054504},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33671581745147705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33531811833381653},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3251042366027832},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.12028282880783081},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.11160668730735779},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08249148726463318},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.04878678917884827},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2018.2838140","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2018.2838140","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:29994133","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29994133","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W15962362","https://openalex.org/W175700287","https://openalex.org/W205960552","https://openalex.org/W1512098439","https://openalex.org/W1532476685","https://openalex.org/W1551209770","https://openalex.org/W1558808826","https://openalex.org/W1621799579","https://openalex.org/W1967934524","https://openalex.org/W1970576786","https://openalex.org/W1970722248","https://openalex.org/W2035720976","https://openalex.org/W2039402388","https://openalex.org/W2061879449","https://openalex.org/W2067563600","https://openalex.org/W2072531725","https://openalex.org/W2075336646","https://openalex.org/W2086326315","https://openalex.org/W2091825929","https://openalex.org/W2095895508","https://openalex.org/W2097308346","https://openalex.org/W2097939965","https://openalex.org/W2098957565","https://openalex.org/W2105608756","https://openalex.org/W2107791152","https://openalex.org/W2112545207","https://openalex.org/W2115975329","https://openalex.org/W2116810533","https://openalex.org/W2117154949","https://openalex.org/W2117990954","https://openalex.org/W2119821739","https://openalex.org/W2127459401","https://openalex.org/W2135106139","https://openalex.org/W2137557016","https://openalex.org/W2139224857","https://openalex.org/W2139451965","https://openalex.org/W2140095548","https://openalex.org/W2141642784","https://openalex.org/W2142114945","https://openalex.org/W2142623206","https://openalex.org/W2143570267","https://openalex.org/W2144902422","https://openalex.org/W2145295623","https://openalex.org/W2146068631","https://openalex.org/W2153635508","https://openalex.org/W2155319834","https://openalex.org/W2160840682","https://openalex.org/W2165874743","https://openalex.org/W2165966284","https://openalex.org/W2171792531","https://openalex.org/W2410894298","https://openalex.org/W2744972366","https://openalex.org/W2912728463","https://openalex.org/W4239510810","https://openalex.org/W6608357910","https://openalex.org/W6630527977","https://openalex.org/W6641446668","https://openalex.org/W6674478206","https://openalex.org/W6674525294","https://openalex.org/W6674927946","https://openalex.org/W6677328822","https://openalex.org/W6677346259","https://openalex.org/W6679165223","https://openalex.org/W6680272842","https://openalex.org/W6680614108","https://openalex.org/W6681302627","https://openalex.org/W6681363145","https://openalex.org/W6683152347","https://openalex.org/W6685152693","https://openalex.org/W6759001517"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W141820298","https://openalex.org/W148178222","https://openalex.org/W4378770497","https://openalex.org/W2049584446","https://openalex.org/W2104657898","https://openalex.org/W2079781215","https://openalex.org/W1948992892","https://openalex.org/W2064404759","https://openalex.org/W1886884218"],"abstract_inverted_index":{"Kernel":[0],"support":[1,30],"vector":[2],"machines":[3],"(SVMs)":[4],"deliver":[5],"state-of-the-art":[6,184],"results":[7,165],"in":[8,22,111,133],"many":[9],"real-world":[10],"nonlinear":[11,75],"classification":[12],"problems,":[13],"but":[14,44],"the":[15,35,96,99,102,116,120,144,147],"computational":[16],"cost":[17],"can":[18,138],"be":[19,139],"quite":[20],"demanding":[21],"order":[23],"to":[24,41,63,77,127,141],"maintain":[25],"a":[26,56,74,78,128,175],"large":[27,42],"number":[28],"of":[29,101,119,146,155,161],"vectors.":[31],"Linear":[32],"SVM,":[33],"on":[34,68,115],"other":[36],"hand,":[37],"is":[38,170],"highly":[39],"scalable":[40],"data":[43],"only":[45],"suited":[46],"for":[47,186],"linearly":[48],"separable":[49],"problems.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54,124],"propose":[55],"novel":[57],"approach":[58,72,151,169],"called":[59],"low-rank":[60,91,148],"linearized":[61],"SVM":[62,67,76,157],"scale":[64],"up":[65],"kernel":[66,85,90,108,156],"limited":[69],"resources.":[70],"Our":[71,150],"transforms":[73],"linear":[79,162],"one":[80],"via":[81],"an":[82],"approximate":[83,103],"empirical":[84],"map":[86],"computed":[87],"from":[88],"efficient":[89],"decompositions.":[92],"We":[93],"theoretically":[94],"analyze":[95],"gap":[97],"between":[98,178],"solutions":[100],"and":[104,158,173,181],"optimal":[105],"rank-":[106],"k":[107],"map,":[109],"which":[110,134],"turn":[112],"provides":[113],"guidance":[114],"sampling":[117],"scheme":[118],"Nystr\u00f6m":[121],"approximation.":[122],"Furthermore,":[123],"extend":[125],"it":[126],"semisupervised":[129],"metric":[130],"learning":[131],"scenario":[132],"partially":[135],"labeled":[136],"samples":[137],"exploited":[140],"further":[142],"improve":[143],"quality":[145],"embedding.":[149],"inherits":[152],"rich":[153],"representability":[154,180],"high":[159],"efficiency":[160],"SVM.":[163],"Experimental":[164],"demonstrate":[166],"that":[167],"our":[168],"more":[171],"robust":[172],"achieves":[174],"better":[176],"tradeoff":[177],"model":[179],"scalability":[182],"against":[183],"algorithms":[185],"large-scale":[187],"SVMs.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}