{"id":"https://openalex.org/W2891862966","doi":"https://doi.org/10.1109/icassp.2018.8461509","title":"Online Multi-Kernel Learning with Orthogonal Random Features","display_name":"Online Multi-Kernel Learning with Orthogonal Random Features","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2891862966","doi":"https://doi.org/10.1109/icassp.2018.8461509","mag":"2891862966"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2018.8461509","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8461509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5019337064","display_name":"Yanning Shen","orcid":"https://orcid.org/0000-0002-7333-893X"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanning Shen","raw_affiliation_strings":["Dept. of ECE and DTC, University of Minnesota, Minneapolis, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of ECE and DTC, University of Minnesota, Minneapolis, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100783476","display_name":"Tianyi Chen","orcid":"https://orcid.org/0000-0003-3477-1439"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Chen","raw_affiliation_strings":["Dept. of ECE and DTC, University of Minnesota, Minneapolis, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of ECE and DTC, University of Minnesota, Minneapolis, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026758314","display_name":"Georgios B. Giannakis","orcid":"https://orcid.org/0000-0002-0196-0260"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Georgios B. Giannakis","raw_affiliation_strings":["Dept. of ECE and DTC, University of Minnesota, Minneapolis, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of ECE and DTC, University of Minnesota, Minneapolis, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":1.1009,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.81012122,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"6","issue":null,"first_page":"6289","last_page":"6293"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9986000061035156,"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":0.9969000220298767,"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/computer-science","display_name":"Computer science","score":0.7541818618774414},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6851657629013062},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6169504523277283},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.578795313835144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5728744268417358},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.5396454930305481},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.52818763256073},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.5139357447624207},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.48799246549606323},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.4826808273792267},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.4741460680961609},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4735024571418762},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.4295438826084137},{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.4180568754673004},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.30299073457717896},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19190630316734314},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08883512020111084}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7541818618774414},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6851657629013062},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6169504523277283},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.578795313835144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5728744268417358},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.5396454930305481},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.52818763256073},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.5139357447624207},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.48799246549606323},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.4826808273792267},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.4741460680961609},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4735024571418762},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.4295438826084137},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.4180568754673004},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.30299073457717896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19190630316734314},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08883512020111084},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2018.8461509","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8461509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1510073064","https://openalex.org/W1570963478","https://openalex.org/W2029171653","https://openalex.org/W2056277442","https://openalex.org/W2121033924","https://openalex.org/W2121423746","https://openalex.org/W2121990650","https://openalex.org/W2139320579","https://openalex.org/W2144902422","https://openalex.org/W2150621701","https://openalex.org/W2408432900","https://openalex.org/W2608018571","https://openalex.org/W2785217227","https://openalex.org/W2949384808","https://openalex.org/W2962737466","https://openalex.org/W3120740533","https://openalex.org/W4205841652","https://openalex.org/W4292022450","https://openalex.org/W4298876635","https://openalex.org/W6677768686","https://openalex.org/W6678059867","https://openalex.org/W6681302627","https://openalex.org/W6714150371","https://openalex.org/W6732811253"],"related_works":["https://openalex.org/W3100948281","https://openalex.org/W3013206934","https://openalex.org/W1983263273","https://openalex.org/W4291669689","https://openalex.org/W2071590642","https://openalex.org/W2371064519","https://openalex.org/W1976730005","https://openalex.org/W2035367180","https://openalex.org/W2055608878","https://openalex.org/W2001173190"],"abstract_inverted_index":{"Kernel-based":[0],"methods":[1],"have":[2],"well-appreciated":[3],"performance":[4],"in":[5,39],"various":[6],"nonlinear":[7,70],"learning":[8,31,64],"tasks.":[9],"Most":[10],"of":[11,98],"them":[12],"rely":[13],"on":[14,88],"a":[15,43],"preselected":[16],"kernel,":[17],"whose":[18],"prudent":[19],"choice":[20],"presumes":[21],"task-specific":[22],"prior":[23],"information.":[24],"To":[25],"cope":[26],"with":[27],"this":[28],"limitation,":[29],"multi-kernel":[30,63],"has":[32],"gained":[33],"popularity":[34],"thanks":[35],"to":[36,66,94],"its":[37,53],"flexibility":[38],"choosing":[40],"kernels":[41],"from":[42],"prescribed":[44],"kernel":[45],"dictionary.":[46],"Leveraging":[47],"the":[48,57,68,73,79,96,99],"random":[49],"feature":[50],"approximation":[51],"and":[52],"recent":[54],"orthogonality-promoting":[55],"variant,":[56],"present":[58],"contribution":[59],"develops":[60],"an":[61],"online":[62],"scheme":[65],"infer":[67],"intended":[69],"function":[71],"`on":[72],"fly.'":[74],"Performance":[75],"analysis":[76],"shows":[77],"that":[78],"novel":[80],"algorithm":[81],"can":[82],"afford":[83],"sublinear":[84],"regret.":[85],"Numerical":[86],"tests":[87],"real":[89],"datasets":[90],"are":[91],"carried":[92],"out":[93],"showcase":[95],"effectiveness":[97],"proposed":[100],"algorithms.":[101]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
