{"id":"https://openalex.org/W2426604946","doi":"https://doi.org/10.1109/icde.2016.7498284","title":"The CRO kernel: Using Concomitant Rank Order hashes for sparse high dimensional randomized feature maps","display_name":"The CRO kernel: Using Concomitant Rank Order hashes for sparse high dimensional randomized feature maps","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2426604946","doi":"https://doi.org/10.1109/icde.2016.7498284","mag":"2426604946"},"language":"en","primary_location":{"id":"doi:10.1109/icde.2016.7498284","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498284","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5088426443","display_name":"Kave Eshghi","orcid":"https://orcid.org/0000-0003-3374-3145"},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kave Eshghi","raw_affiliation_strings":["Hewlett Packard Labs, Palo Alto, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hewlett Packard Labs, Palo Alto, CA","institution_ids":["https://openalex.org/I1324840837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027573145","display_name":"Mehran Kafai","orcid":"https://orcid.org/0000-0003-0238-7010"},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mehran Kafai","raw_affiliation_strings":["Hewlett Packard Labs, Palo Alto, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hewlett Packard Labs, Palo Alto, CA","institution_ids":["https://openalex.org/I1324840837"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1324840837"],"apc_list":null,"apc_paid":null,"fwci":0.3019,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61983085,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"11","issue":null,"first_page":"721","last_page":"730"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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/T10057","display_name":"Face and Expression Recognition","score":0.9994999766349792,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9929999709129333,"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/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.7779418230056763},{"id":"https://openalex.org/keywords/string-kernel","display_name":"String kernel","score":0.7348928451538086},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.7247395515441895},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7066481113433838},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.6628109216690063},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.6303080916404724},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.6143386363983154},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.593641459941864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5638731122016907},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.47475412487983704},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.47427377104759216},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45450952649116516},{"id":"https://openalex.org/keywords/graph-kernel","display_name":"Graph kernel","score":0.43343430757522583},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4129742383956909},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3720472455024719},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.09141772985458374}],"concepts":[{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.7779418230056763},{"id":"https://openalex.org/C55851704","wikidata":"https://www.wikidata.org/wiki/Q7623983","display_name":"String kernel","level":5,"score":0.7348928451538086},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.7247395515441895},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7066481113433838},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.6628109216690063},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.6303080916404724},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.6143386363983154},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.593641459941864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5638731122016907},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47475412487983704},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.47427377104759216},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45450952649116516},{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.43343430757522583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4129742383956909},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3720472455024719},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.09141772985458374}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icde.2016.7498284","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498284","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W205795741","https://openalex.org/W1964357740","https://openalex.org/W1973318348","https://openalex.org/W1997075100","https://openalex.org/W2019336343","https://openalex.org/W2039240309","https://openalex.org/W2050025922","https://openalex.org/W2061668675","https://openalex.org/W2070996757","https://openalex.org/W2096613134","https://openalex.org/W2109235804","https://openalex.org/W2112796928","https://openalex.org/W2118563516","https://openalex.org/W2118585731","https://openalex.org/W2125378448","https://openalex.org/W2127179243","https://openalex.org/W2139451965","https://openalex.org/W2143570267","https://openalex.org/W2144902422","https://openalex.org/W2146897752","https://openalex.org/W2153635508","https://openalex.org/W2155319834","https://openalex.org/W2786209651","https://openalex.org/W2952504976","https://openalex.org/W4293865401","https://openalex.org/W4294767349","https://openalex.org/W6608318307","https://openalex.org/W6674807959","https://openalex.org/W6677656871","https://openalex.org/W6678556256","https://openalex.org/W6678988221","https://openalex.org/W6681302627","https://openalex.org/W6681363145","https://openalex.org/W6683152347","https://openalex.org/W6764039759"],"related_works":["https://openalex.org/W3013206934","https://openalex.org/W3100948281","https://openalex.org/W1983263273","https://openalex.org/W2130792056","https://openalex.org/W2574115973","https://openalex.org/W4311138679","https://openalex.org/W2090782076","https://openalex.org/W2426604946","https://openalex.org/W4401177601","https://openalex.org/W1590832708"],"abstract_inverted_index":{"Kernel":[0],"methods":[1,41],"have":[2,28],"been":[3],"shown":[4],"to":[5,30,38,43,73],"be":[6,31],"effective":[7],"for":[8,69,111,149,166],"many":[9],"machine":[10],"learning":[11],"tasks":[12],"such":[13],"as":[14],"classification,":[15],"clustering":[16],"and":[17,84],"regression.":[18],"In":[19],"particular,":[20],"support":[21,93],"vector":[22],"machines":[23],"with":[24,89,179],"the":[25,45,49,53,56,62,66,79,90,100,108,142,146,151,156,173,176,180,191],"RBF":[26,109,177],"kernel":[27,40,63,67,105,110,178],"proved":[29],"powerful":[32],"classification":[33,85],"tools.":[34],"The":[35],"standard":[36,200],"way":[37],"apply":[39],"is":[42,59,76,87,162],"use":[44],"`kernel":[46],"trick',":[47],"where":[48],"inner":[50,138],"product":[51,139],"of":[52,81,92,158,175,182,193,199],"vectors":[54,83,136,161],"in":[55,78],"feature":[57,122,135,160],"space":[58],"computed":[60],"via":[61],"function.":[64],"Using":[65,145],"trick":[68],"SVMs,":[70],"however,":[71],"leads":[72],"training":[74],"that":[75,86,106,130,155],"quadratic":[77],"number":[80,91,198],"input":[82,114,169],"linear":[88,187],"vectors.":[94,115],"We":[95,116,189],"introduce":[96,118],"a":[97,119,183,197],"new":[98,120],"kernel,":[99],"CRO":[101,143,152],"(Concomitant":[102],"Rank":[103],"Order)":[104],"approximates":[107],"unit":[112,167],"length":[113,168],"also":[117],"randomized":[121],"map,":[123],"based":[124],"on":[125],"concomitant":[126],"rank":[127],"order":[128],"hashing,":[129],"produces":[131],"sparse,":[132],"high":[133,185],"dimensional":[134,186],"whose":[137],"asymptotically":[140],"equals":[141],"kernel.":[144,188],"Hadamard":[147],"transform":[148],"computing":[150,159],"hashes":[153],"ensures":[154],"cost":[157],"very":[163],"low.":[164],"Thus,":[165],"vectors,":[170],"we":[171],"get":[172],"accuracy":[174],"efficiency":[181],"sparse":[184],"show":[190],"efficacy":[192],"our":[194],"approach":[195],"using":[196],"datasets.":[201]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}