{"id":"https://openalex.org/W2166537789","doi":"https://doi.org/10.1109/ijcnn.2014.6889415","title":"Feature ensemble learning based on sparse autoencoders for image classification","display_name":"Feature ensemble learning based on sparse autoencoders for image classification","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W2166537789","doi":"https://doi.org/10.1109/ijcnn.2014.6889415","mag":"2166537789"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2014.6889415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","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/A5080467362","display_name":"Yaping Lu","orcid":"https://orcid.org/0009-0000-5201-180X"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaping Lu","raw_affiliation_strings":["School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425448","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0001-7914-0679"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075719824","display_name":"Bangjun Wang","orcid":"https://orcid.org/0000-0003-1372-2486"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bangjun Wang","raw_affiliation_strings":["School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110681087","display_name":"Jiwen Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiwen Yang","raw_affiliation_strings":["School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5080467362"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":1.4632,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.86274373,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.996999979019165,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.996999979019165,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9957000017166138,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9951000213623047,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7246444225311279},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7232688069343567},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7040079236030579},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6412510275840759},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5784012079238892},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5406038761138916},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47092193365097046},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46874597668647766},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4665961265563965}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7246444225311279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7232688069343567},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7040079236030579},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6412510275840759},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5784012079238892},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5406038761138916},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47092193365097046},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46874597668647766},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4665961265563965},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2014.6889415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","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":16,"referenced_works":["https://openalex.org/W61567823","https://openalex.org/W2025768430","https://openalex.org/W2051952642","https://openalex.org/W2089340591","https://openalex.org/W2100495367","https://openalex.org/W2102734279","https://openalex.org/W2122892819","https://openalex.org/W2134905716","https://openalex.org/W2136922672","https://openalex.org/W2158275940","https://openalex.org/W2165720259","https://openalex.org/W2172174689","https://openalex.org/W2487087946","https://openalex.org/W4231109964","https://openalex.org/W6602592809","https://openalex.org/W6684753728"],"related_works":["https://openalex.org/W3147584709","https://openalex.org/W2977677679","https://openalex.org/W2373792516","https://openalex.org/W1992327129","https://openalex.org/W2905271011","https://openalex.org/W3164948662","https://openalex.org/W4289536128","https://openalex.org/W3153597579","https://openalex.org/W4309346246","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Deep":[0],"networks":[1],"are":[2,22,127,173,188],"well":[3],"known":[4],"for":[5,94],"their":[6],"powerful":[7],"function":[8],"approximations.":[9],"To":[10,72,150],"train":[11,99],"a":[12,29,85,145,156],"deep":[13,30],"network":[14],"efficiently,":[15],"greedy":[16],"layer-wise":[17],"pre-training":[18],"and":[19,59,136,153,167,185],"fine":[20,134],"tuning":[21],"required.":[23],"Typically,":[24],"pre-training,":[25],"aiming":[26],"to":[27,52,66,175,192],"initialize":[28],"network,":[31],"is":[32,51,140,165],"implemented":[33],"via":[34],"unsupervised":[35],"feature":[36,40,86],"learning,":[37],"with":[38,64,133,190],"multiple":[39,163],"representations":[41,78,106],"generated.":[42],"However,":[43],"in":[44],"general":[45],"only":[46],"the":[47,62,67,77,105,117,122,137,160,177,183],"last":[48,118],"layer":[49,119],"representation":[50],"be":[53],"employed":[54],"because":[55],"of":[56,69,76,79,107,111,147,155,162],"its":[57],"abstraction":[58],"compactness":[60],"being":[61],"best":[63],"comparisons":[65,191],"ones":[68],"lower":[70],"layers.":[71],"make":[73],"full":[74],"use":[75],"all":[80],"layers,":[81,109],"this":[82],"paper":[83],"proposes":[84],"ensemble":[87,161],"learning":[88],"method":[89],"based":[90],"on":[91,182],"sparse":[92],"autoencoders":[93,132],"image":[95],"classification.":[96],"Specifically,":[97],"we":[98],"three":[100,123,178],"softmax":[101,124,158],"classifiers":[102,164],"by":[103,115,129,142],"using":[104,144],"different":[108],"instead":[110],"one":[112,139],"classifier":[113],"trained":[114],"applying":[116],"representation.":[120],"Of":[121],"classifiers,":[125],"two":[126,148],"obtained":[128,141],"training":[130],"stacked":[131],"tuning,":[135],"other":[138,193],"directly":[143],"concatenation":[146],"representations.":[149],"improve":[151],"accuracy":[152],"stability":[154],"single":[157],"classifier,":[159],"considered,":[166],"some":[168],"Naive":[169],"Bayes":[170],"combination":[171],"rules":[172],"introduced":[174],"integrate":[176],"classifiers.":[179],"Experimental":[180],"results":[181],"MNTST":[184],"COIL":[186],"datasets":[187],"presented,":[189],"classification":[194],"methods.":[195]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
