{"id":"https://openalex.org/W2305260429","doi":"https://doi.org/10.1109/tits.2015.2495342","title":"Discriminative Weighted Sparse Partial Least Squares for Human Detection","display_name":"Discriminative Weighted Sparse Partial Least Squares for Human Detection","publication_year":2015,"publication_date":"2015-11-13","ids":{"openalex":"https://openalex.org/W2305260429","doi":"https://doi.org/10.1109/tits.2015.2495342","mag":"2305260429"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2015.2495342","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2015.2495342","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-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/A5100763663","display_name":"Qiming Li","orcid":"https://orcid.org/0000-0001-7289-0901"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiming Li","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395059","display_name":"Yan Yan","orcid":"https://orcid.org/0000-0002-3674-7160"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Yan","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044594971","display_name":"Hanzi Wang","orcid":"https://orcid.org/0000-0002-6913-9786"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanzi Wang","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100763663"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":1.1046,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.85362299,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"17","issue":"4","first_page":"1062","last_page":"1071"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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":0.9936000108718872,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.974399983882904,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8602818250656128},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6854826807975769},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6759630441665649},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6425641179084778},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6418445110321045},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5947335362434387},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.5540197491645813},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29968011379241943}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8602818250656128},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6854826807975769},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6759630441665649},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6425641179084778},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6418445110321045},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5947335362434387},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.5540197491645813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29968011379241943},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2015.2495342","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2015.2495342","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.75}],"awards":[{"id":"https://openalex.org/G2930454911","display_name":null,"funder_award_id":"61472334","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3313464985","display_name":null,"funder_award_id":"61571379","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4628247079","display_name":null,"funder_award_id":"61170179","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6177911381","display_name":null,"funder_award_id":"20720130720","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1975900269","https://openalex.org/W1986905809","https://openalex.org/W1989684337","https://openalex.org/W1990378711","https://openalex.org/W1999853363","https://openalex.org/W2031454541","https://openalex.org/W2034779469","https://openalex.org/W2039051707","https://openalex.org/W2044465660","https://openalex.org/W2062236848","https://openalex.org/W2077513643","https://openalex.org/W2084997728","https://openalex.org/W2096112371","https://openalex.org/W2098064689","https://openalex.org/W2104606180","https://openalex.org/W2120419212","https://openalex.org/W2125556102","https://openalex.org/W2127420331","https://openalex.org/W2134778014","https://openalex.org/W2137225583","https://openalex.org/W2140090057","https://openalex.org/W2141357020","https://openalex.org/W2151049637","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2162741153","https://openalex.org/W2168356304","https://openalex.org/W2170101770","https://openalex.org/W2170917242","https://openalex.org/W2272000905","https://openalex.org/W2534262995","https://openalex.org/W2548197316","https://openalex.org/W4205687621","https://openalex.org/W6646904105","https://openalex.org/W6674636832","https://openalex.org/W6674977583","https://openalex.org/W6675436802","https://openalex.org/W6680862486","https://openalex.org/W6684811926"],"related_works":["https://openalex.org/W2776189519","https://openalex.org/W2963942857","https://openalex.org/W2587438737","https://openalex.org/W2903629662","https://openalex.org/W2756442679","https://openalex.org/W3201045222","https://openalex.org/W2902082647","https://openalex.org/W2022885056","https://openalex.org/W2897276373","https://openalex.org/W3008576279","https://openalex.org/W3200086554","https://openalex.org/W2794032852","https://openalex.org/W2910587455","https://openalex.org/W3063257891","https://openalex.org/W2083666679","https://openalex.org/W2997896449","https://openalex.org/W2193513848","https://openalex.org/W1148494366","https://openalex.org/W2591638990","https://openalex.org/W2755299938"],"abstract_inverted_index":{"Channel":[0],"feature":[1,44,71,120],"detectors":[2],"have":[3],"shown":[4],"great":[5],"advantages":[6],"in":[7,74],"human":[8,19,50,146,164],"detection.":[9,51],"However,":[10],"a":[11,34,59,78,84,92],"large":[12],"pool":[13],"of":[14,139],"channel":[15,127],"features":[16,128,141,152],"extracted":[17],"for":[18,43,119],"detection":[20],"usually":[21],"contains":[22],"many":[23],"redundant":[24],"and":[25,46,117,135,157,174],"irrelevant":[26],"features.":[27],"To":[28],"address":[29],"this":[30],"issue,":[31],"we":[32,64,82,123],"propose":[33,65],"robust":[35,79,106],"discriminative":[36,85,93],"weighted":[37,87],"sparse":[38,67,107],"partial":[39,53],"least":[40,54,88],"square":[41,89],"approach":[42,171],"selection":[45],"apply":[47],"it":[48],"to":[49,69,76,97,129],"Unlike":[52],"squares":[55],"(PLS),":[56],"which":[57],"is":[58,95,110,172],"straightforward":[60],"dimensionality":[61],"reduction":[62],"technique,":[63],"using":[66],"PLS":[68],"achieve":[70],"selection.":[72,121],"Furthermore,":[73],"order":[75],"obtain":[77],"latent":[80,115],"matrix,":[81],"formulate":[83],"regularized":[86],"problem,":[90],"where":[91],"term":[94],"incorporated":[96],"effectively":[98],"distinguish":[99],"positive":[100],"samples":[101],"from":[102],"negative":[103],"samples.":[104],"A":[105],"weight":[108],"matrix":[109,116],"trained":[111,148],"based":[112],"on":[113,161],"the":[114,125,131,137,150,169],"used":[118],"Finally,":[122],"use":[124],"selected":[126,140,151],"train":[130],"boosted":[132],"decision":[133],"trees":[134],"incorporate":[136],"weights":[138],"with":[142],"each":[143],"tree.":[144],"The":[145],"detector":[147],"by":[149],"can":[153],"preserve":[154],"high":[155],"robustness":[156],"discriminativeness.":[158],"Experimental":[159],"results":[160],"some":[162],"challenging":[163],"data":[165],"sets":[166],"demonstrate":[167],"that":[168],"proposed":[170],"effective":[173],"achieves":[175],"state-of-the-art":[176],"performance.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}