{"id":"https://openalex.org/W2413797286","doi":"https://doi.org/10.1109/acpr.2015.7486606","title":"Stacked partial least squares regression for image classification","display_name":"Stacked partial least squares regression for image classification","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2413797286","doi":"https://doi.org/10.1109/acpr.2015.7486606","mag":"2413797286"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486606","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5056041069","display_name":"Ryoma Hasegawa","orcid":null},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryoma Hasegawa","raw_affiliation_strings":["Meijo University, Tempaku-ku, Nagoya, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo University, Tempaku-ku, Nagoya, Japan","institution_ids":["https://openalex.org/I96636082"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103163418","display_name":"Kazuhiro Hotta","orcid":"https://orcid.org/0000-0002-5675-8713"},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhiro Hotta","raw_affiliation_strings":["Meijo University, Tempaku-ku, Nagoya, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo University, Tempaku-ku, Nagoya, Japan","institution_ids":["https://openalex.org/I96636082"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5056041069"],"corresponding_institution_ids":["https://openalex.org/I96636082"],"apc_list":null,"apc_paid":null,"fwci":0.3682,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.71389568,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"9","issue":null,"first_page":"765","last_page":"769"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9965999722480774,"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":0.9965999722480774,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/mnist-database","display_name":"MNIST database","score":0.906342625617981},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8091503381729126},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.8055484294891357},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7408525347709656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7386500835418701},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7377041578292847},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7288996577262878},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5458675622940063},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5345607995986938},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.478836327791214},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4743971824645996},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3365294933319092},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28031885623931885},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13783374428749084},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09525182843208313}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.906342625617981},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8091503381729126},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.8055484294891357},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7408525347709656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7386500835418701},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7377041578292847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7288996577262878},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5458675622940063},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5345607995986938},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.478836327791214},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4743971824645996},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3365294933319092},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28031885623931885},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13783374428749084},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09525182843208313},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2015.7486606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486606","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2016053056","https://openalex.org/W2062118960","https://openalex.org/W2097117768","https://openalex.org/W2102017903","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2118585731","https://openalex.org/W2145287260","https://openalex.org/W2147414309","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2179352600","https://openalex.org/W2294059674","https://openalex.org/W2534262995","https://openalex.org/W2963911037","https://openalex.org/W3102431071","https://openalex.org/W4239072543","https://openalex.org/W6638444622","https://openalex.org/W6674914833","https://openalex.org/W6676297131","https://openalex.org/W6676338569","https://openalex.org/W6677656871","https://openalex.org/W6684191040","https://openalex.org/W6696761078","https://openalex.org/W6785107657"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W4387163578","https://openalex.org/W4206451978","https://openalex.org/W3195622388","https://openalex.org/W4200096682","https://openalex.org/W3041443116","https://openalex.org/W2429686810","https://openalex.org/W2371059786","https://openalex.org/W2565656575"],"abstract_inverted_index":{"In":[0,79],"recent":[1,60],"years,":[2],"the":[3,18,29,34,38,84,124,135,143],"researches":[4],"based":[5],"on":[6,123],"Convolutional":[7],"Neural":[8],"Network":[9],"(CNN)":[10],"have":[11],"been":[12,48],"doing":[13],"in":[14,20,51,56,59,112],"computer":[15,57],"vision":[16,58],"after":[17],"success":[19],"ILSVRC":[21],"2012.":[22],"Hierarchical":[23],"feature":[24,88,93],"extraction":[25,89,94],"is":[26,53,121,140],"one":[27],"of":[28,86,90],"reasons":[30],"why":[31],"CNN":[32,91,115,133],"gives":[33],"state-of-the-art":[35,144],"performance.":[36],"On":[37],"other":[39],"hand,":[40],"Partial":[41],"Least":[42],"Squares":[43],"(PLS)":[44],"Regression":[45],"which":[46],"has":[47],"widely":[49],"used":[50,55,66],"chemo-metrics":[52],"also":[54],"years.":[61],"If":[62],"class":[63],"labels":[64],"are":[65],"as":[67],"objective":[68],"variables":[69],"for":[70,77,96],"PLS,":[71],"PLS":[72,99],"can":[73],"extract":[74],"features":[75,110],"suitable":[76,95],"classification.":[78],"this":[80],"paper,":[81],"we":[82],"combine":[83],"idea":[85],"hierarchical":[87],"with":[92,134],"classification":[97],"by":[98],"and":[100,139],"propose":[101],"a":[102],"new":[103],"method":[104,120,128],"called":[105],"Stacked":[106],"PLS.":[107,117],"It":[108],"extracts":[109],"hierarchically":[111],"reference":[113],"to":[114,142],"using":[116],"The":[118],"proposed":[119],"evaluated":[122],"MNIST":[125],"dataset.":[126],"Our":[127],"gave":[129],"higher":[130],"performance":[131],"than":[132],"same":[136],"network":[137],"architecture":[138],"comparable":[141],"methods.":[145]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
