{"id":"https://openalex.org/W2769773494","doi":"https://doi.org/10.1109/tcyb.2017.2764099","title":"Sparse Bayesian Learning-Based Kernel Poisson Regression","display_name":"Sparse Bayesian Learning-Based Kernel Poisson Regression","publication_year":2017,"publication_date":"2017-11-28","ids":{"openalex":"https://openalex.org/W2769773494","doi":"https://doi.org/10.1109/tcyb.2017.2764099","mag":"2769773494","pmid":"https://pubmed.ncbi.nlm.nih.gov/29990073"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2017.2764099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2017.2764099","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","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/A5013880628","display_name":"Yuheng Jia","orcid":"https://orcid.org/0000-0002-3907-6550"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yuheng Jia","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-3907-6550","affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008386708","display_name":"Sam Kwong","orcid":"https://orcid.org/0000-0001-7484-7261"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]},{"id":"https://openalex.org/I4210105229","display_name":"City University of Hong Kong, Shenzhen Research Institute","ror":"https://ror.org/00xc0ma20","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210105229"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Sam Kwong","raw_affiliation_strings":["City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-7484-7261","affiliations":[{"raw_affiliation_string":"City University of Hong Kong Shenzhen Research Institute, Shenzhen, China","institution_ids":["https://openalex.org/I168719708","https://openalex.org/I4210105229"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081114494","display_name":"Wenhui Wu","orcid":"https://orcid.org/0000-0002-0416-7719"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wenhui Wu","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114377922","display_name":"Ran Wang","orcid":"https://orcid.org/0000-0002-2586-5604"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Wang","raw_affiliation_strings":["College of Mathematics and Statistics, Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-2586-5604","affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050309466","display_name":"Wei Gao","orcid":"https://orcid.org/0000-0001-7429-5495"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wei Gao","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0001-7429-5495","affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.595,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.87975823,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"49","issue":"1","first_page":"56","last_page":"68"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","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/T11901","display_name":"Bayesian Methods and Mixture Models","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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9940000176429749,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.6829298734664917},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.612839937210083},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5238835215568542},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5228524804115295},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.5220910310745239},{"id":"https://openalex.org/keywords/poisson-regression","display_name":"Poisson regression","score":0.519006609916687},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5097350478172302},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.48417216539382935},{"id":"https://openalex.org/keywords/conjugate-prior","display_name":"Conjugate prior","score":0.47692009806632996},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.4741120934486389},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45246458053588867},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4348416328430176},{"id":"https://openalex.org/keywords/count-data","display_name":"Count data","score":0.43050503730773926},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4161759614944458},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41561371088027954},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.40520691871643066},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.3687577247619629},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36469119787216187},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3499680459499359},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24933815002441406}],"concepts":[{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.6829298734664917},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.612839937210083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5238835215568542},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5228524804115295},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.5220910310745239},{"id":"https://openalex.org/C73269764","wikidata":"https://www.wikidata.org/wiki/Q954529","display_name":"Poisson regression","level":3,"score":0.519006609916687},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5097350478172302},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.48417216539382935},{"id":"https://openalex.org/C26004113","wikidata":"https://www.wikidata.org/wiki/Q3711784","display_name":"Conjugate prior","level":4,"score":0.47692009806632996},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.4741120934486389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45246458053588867},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4348416328430176},{"id":"https://openalex.org/C33643355","wikidata":"https://www.wikidata.org/wiki/Q5176731","display_name":"Count data","level":3,"score":0.43050503730773926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4161759614944458},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41561371088027954},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.40520691871643066},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3687577247619629},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36469119787216187},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3499680459499359},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24933815002441406},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"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/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tcyb.2017.2764099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2017.2764099","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","raw_type":"journal-article"},{"id":"pmid:29990073","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29990073","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 cybernetics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4835894923","display_name":"\u591a\u6807\u8bb0\u95ee\u9898\u7684\u4e0d\u786e\u5b9a\u6027\u5206\u6790\u4e0e\u4e3b\u52a8\u5b66\u4e60\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61772344","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6703717299","display_name":null,"funder_award_id":"61402460","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7678096932","display_name":"\u9762\u5411\u89c6\u89c9\u611f\u77e5\u548c\u79fb\u52a8\u7ec8\u7aef\u7684\u9ad8\u6548\u89c6\u9891\u7f16\u7801\u4f18\u5316\u7814\u7a76","funder_award_id":"61672443","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W165787321","https://openalex.org/W1528905581","https://openalex.org/W1567512734","https://openalex.org/W1591346633","https://openalex.org/W1746819321","https://openalex.org/W1965801346","https://openalex.org/W1968746595","https://openalex.org/W1985516812","https://openalex.org/W2008717648","https://openalex.org/W2015725852","https://openalex.org/W2018798674","https://openalex.org/W2038974670","https://openalex.org/W2060831261","https://openalex.org/W2078381696","https://openalex.org/W2088929512","https://openalex.org/W2111414067","https://openalex.org/W2121024352","https://openalex.org/W2123175289","https://openalex.org/W2125833791","https://openalex.org/W2133871228","https://openalex.org/W2135046866","https://openalex.org/W2135980307","https://openalex.org/W2137226992","https://openalex.org/W2137559179","https://openalex.org/W2146000945","https://openalex.org/W2146247053","https://openalex.org/W2148154358","https://openalex.org/W2150057984","https://openalex.org/W2151386286","https://openalex.org/W2171980229","https://openalex.org/W2254069717","https://openalex.org/W2500878022","https://openalex.org/W2541389513","https://openalex.org/W2594551469","https://openalex.org/W2801490189","https://openalex.org/W2963762195","https://openalex.org/W4211049957","https://openalex.org/W4285719527","https://openalex.org/W6606745025","https://openalex.org/W6684942582","https://openalex.org/W6750773745"],"related_works":["https://openalex.org/W2476449081","https://openalex.org/W4298870584","https://openalex.org/W2734174941","https://openalex.org/W4298441262","https://openalex.org/W4306291977","https://openalex.org/W2950950409","https://openalex.org/W2232589144","https://openalex.org/W2404382874","https://openalex.org/W2905505054","https://openalex.org/W1768043922"],"abstract_inverted_index":{"In":[0,27,63],"this":[1,64,75],"paper,":[2,65],"we":[3],"introduce":[4],"a":[5,30],"closed-form":[6,84],"sparse":[7,22,107],"Bayesian":[8,23,28],"kernel":[9],"Poisson":[10,46],"regression":[11,17,143],"(SBKPR)":[12],"model":[13,37,50,95,136],"for":[14],"count":[15,141],"data":[16,142,148],"problems":[18],"based":[19],"on":[20,145],"the":[21,36,42,49,59,66,83,94,100,103,118,123,133],"learning":[24,119],"(SBL)":[25],"approach.":[26],"setting,":[29],"Gaussian":[31,68,89],"prior":[32,90],"is":[33,40,71,91],"given":[34,92],"to":[35,58,73,93],"parameter,":[38],"which":[39,56,79,97,115],"not":[41],"conjugate":[43],"distribution":[44],"of":[45,102],"regression.":[47],"Hence,":[48],"parameters":[51],"cannot":[52],"be":[53,110],"integrated":[54],"analytically,":[55],"leads":[57],"inference":[60],"intractable":[61,77],"problem.":[62],"log-gamma":[67],"approximation":[69],"method":[70],"proposed":[72,104,134],"solve":[74],"analytically":[76],"problem,":[78],"can":[80,98,109,116,137],"give":[81],"out":[82],"solutions.":[85],"Furthermore,":[86],"an":[87],"individual":[88],"parameters,":[96],"enhance":[99],"flexibility":[101],"method.":[105],"Finally,":[106],"solutions":[108],"obtained":[111],"by":[112],"applying":[113],"SBL,":[114],"benefit":[117],"efficiency":[120],"and":[121,149],"reduce":[122],"computational":[124],"time":[125],"in":[126],"practical":[127],"applications.":[128],"Experimental":[129],"results":[130],"demonstrate":[131],"that":[132],"SBKPR":[135],"outperform":[138],"some":[139],"state-of-the-art":[140],"models":[144],"both":[146],"toy":[147],"real-world":[150],"data.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
