{"id":"https://openalex.org/W2131020929","doi":"https://doi.org/10.1145/1645953.1646029","title":"L2 norm regularized feature kernel regression for graph data","display_name":"L2 norm regularized feature kernel regression for graph data","publication_year":2009,"publication_date":"2009-11-02","ids":{"openalex":"https://openalex.org/W2131020929","doi":"https://doi.org/10.1145/1645953.1646029","mag":"2131020929"},"language":"en","primary_location":{"id":"doi:10.1145/1645953.1646029","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1645953.1646029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM conference on Information and knowledge management","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/A5068755696","display_name":"Hongliang Fei","orcid":null},"institutions":[{"id":"https://openalex.org/I146416000","display_name":"University of Kansas","ror":"https://ror.org/001tmjg57","country_code":"US","type":"education","lineage":["https://openalex.org/I146416000"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hongliang Fei","raw_affiliation_strings":["University of Kansas, Lawrence, KS, USA"],"affiliations":[{"raw_affiliation_string":"University of Kansas, Lawrence, KS, USA","institution_ids":["https://openalex.org/I146416000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105352906","display_name":"Jun Huan","orcid":"https://orcid.org/0000-0003-4929-2617"},"institutions":[{"id":"https://openalex.org/I146416000","display_name":"University of Kansas","ror":"https://ror.org/001tmjg57","country_code":"US","type":"education","lineage":["https://openalex.org/I146416000"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Huan","raw_affiliation_strings":["University of Kansas, Lawrence, KS, USA"],"affiliations":[{"raw_affiliation_string":"University of Kansas, Lawrence, KS, USA","institution_ids":["https://openalex.org/I146416000"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068755696"],"corresponding_institution_ids":["https://openalex.org/I146416000"],"apc_list":null,"apc_paid":null,"fwci":2.2965,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.89866685,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"593","last_page":"600"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9975000023841858,"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":0.9975000023841858,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.984499990940094,"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/computer-science","display_name":"Computer science","score":0.6552156209945679},{"id":"https://openalex.org/keywords/graph-kernel","display_name":"Graph kernel","score":0.5783460736274719},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5397186875343323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5290554761886597},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44417011737823486},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.423937052488327},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41597509384155273},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3740260899066925},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.36851710081100464},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35776185989379883},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.3494243621826172},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2857920527458191},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1745748519897461}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6552156209945679},{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.5783460736274719},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5397186875343323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5290554761886597},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44417011737823486},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.423937052488327},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41597509384155273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3740260899066925},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.36851710081100464},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35776185989379883},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.3494243621826172},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2857920527458191},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1745748519897461},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1645953.1646029","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1645953.1646029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM conference on Information and knowledge management","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.566.2903","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.566.2903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ittc.ku.edu/sensornet/publications/FY_07/Fei.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W321468090","https://openalex.org/W1535228720","https://openalex.org/W1576213419","https://openalex.org/W1816257748","https://openalex.org/W1835509607","https://openalex.org/W2039444222","https://openalex.org/W2063978378","https://openalex.org/W2080242873","https://openalex.org/W2096864392","https://openalex.org/W2106969242","https://openalex.org/W2110920461","https://openalex.org/W2118349699","https://openalex.org/W2121727356","https://openalex.org/W2122825543","https://openalex.org/W2135046866","https://openalex.org/W2138019504","https://openalex.org/W2147286743","https://openalex.org/W2148694408","https://openalex.org/W2153037537","https://openalex.org/W2161723275","https://openalex.org/W2164281374","https://openalex.org/W2338175496","https://openalex.org/W2527497076","https://openalex.org/W3023786531","https://openalex.org/W4234698323","https://openalex.org/W4249845687","https://openalex.org/W4292023222"],"related_works":["https://openalex.org/W2090782076","https://openalex.org/W4401177601","https://openalex.org/W2115741655","https://openalex.org/W3008854474","https://openalex.org/W3044028388","https://openalex.org/W3099811568","https://openalex.org/W3046044621","https://openalex.org/W4287862919","https://openalex.org/W1590832708","https://openalex.org/W3013206934"],"abstract_inverted_index":{"Features":[0],"in":[1,146],"many":[2],"real":[3],"world":[4],"applications":[5],"such":[6],"as":[7],"Cheminformatics,":[8],"Bioinformatics":[9],"and":[10,35,37,47,78,136,189],"Information":[11],"Retrieval":[12],"have":[13,30,75,137,179,190],"complex":[14],"internal":[15,52],"structure.":[16],"For":[17],"example,":[18],"frequent":[19],"patterns":[20],"mined":[21],"from":[22],"graph":[23,28],"data":[24,45,73,187],"are":[25,56],"graphs.":[26],"Such":[27],"features":[29,55,70],"different":[31,185],"number":[32],"of":[33,54,71,104,120,126,175,194],"nodes":[34],"edges":[36],"usually":[38,57],"overlap":[39],"with":[40],"each":[41],"other.":[42],"In":[43,59],"conventional":[44,165],"mining":[46],"machine":[48],"learning":[49,66,148,166],"applications,":[50],"the":[51,69,72,83,102,117,121,124,143,147,172,176,192],"structure":[53,119],"ignored.":[58],"this":[60,128],"paper":[61],"we":[62,79,107,130],"consider":[63,171],"a":[64,91,133,151],"supervised":[65],"problem":[67],"where":[68],"set":[74],"intrinsic":[76,85,118],"complexity,":[77,106],"further":[80],"assume":[81],"that":[82,96,115,141,168],"feature":[84,105,144],"complexity":[86,145],"may":[87],"be":[88],"measured":[89],"by":[90,97],"kernel":[92,152],"function.":[93],"We":[94,178],"hypothesize":[95],"regularizing":[98],"model":[99,114],"parameters":[100],"using":[101,150,183],"information":[103,174],"can":[108],"construct":[109],"simple":[110],"yet":[111],"high":[112],"quality":[113],"captures":[116],"data.":[122],"Towards":[123],"end":[125],"testing":[127],"hypothesis,":[129],"focus":[131],"on":[132],"regression":[134,162],"task":[135],"designed":[138],"an":[139],"algorithm":[140,182],"incorporate":[142],"process,":[149],"matrix":[153],"weighted":[154],"L2":[155],"norm":[156],"for":[157],"regularization,":[158],"to":[159],"obtain":[160],"improved":[161],"performance":[163],"over":[164],"methods":[167],"does":[169],"not":[170],"additional":[173],"feature.":[177],"tested":[180],"our":[181,195],"5":[184],"real-world":[186],"sets":[188],"demonstrate":[191],"effectiveness":[193],"method.":[196]},"counts_by_year":[{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}