{"id":"https://openalex.org/W3137470415","doi":"https://doi.org/10.1109/bigdata50022.2020.9378493","title":"Feature-based Distant Domain Transfer Learning","display_name":"Feature-based Distant Domain Transfer Learning","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3137470415","doi":"https://doi.org/10.1109/bigdata50022.2020.9378493","mag":"3137470415"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5028748926","display_name":"Shuteng Niu","orcid":"https://orcid.org/0000-0002-1069-9236"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shuteng Niu","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University","institution_ids":["https://openalex.org/I84475105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103206063","display_name":"Yihao Hu","orcid":"https://orcid.org/0009-0003-3048-3867"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yihao Hu","raw_affiliation_strings":["Department of Computer Science, Duke University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370341","display_name":"Jian Wang","orcid":"https://orcid.org/0000-0001-6043-6971"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Wang","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University","institution_ids":["https://openalex.org/I84475105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100765920","display_name":"Yongxin Liu","orcid":"https://orcid.org/0000-0003-4527-8623"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongxin Liu","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University","institution_ids":["https://openalex.org/I84475105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079301418","display_name":"Houbing Song","orcid":"https://orcid.org/0000-0003-2631-9223"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Houbing Song","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University","institution_ids":["https://openalex.org/I84475105"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5028748926"],"corresponding_institution_ids":["https://openalex.org/I84475105"],"apc_list":null,"apc_paid":null,"fwci":2.8413,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.92697594,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5164","last_page":"5171"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T12676","display_name":"Machine Learning and ELM","score":0.9843999743461609,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9139999747276306,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7728966474533081},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7065860033035278},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6773991584777832},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6596336364746094},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6431646943092346},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6048177480697632},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5956544876098633},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5467653274536133},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5334798097610474},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.42955368757247925},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4136841893196106},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4100651443004608},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40834543108940125},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.22088947892189026},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.161004900932312},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.14242887496948242}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7728966474533081},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7065860033035278},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6773991584777832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6596336364746094},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6431646943092346},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6048177480697632},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5956544876098633},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5467653274536133},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5334798097610474},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.42955368757247925},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4136841893196106},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4100651443004608},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40834543108940125},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22088947892189026},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.161004900932312},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.14242887496948242},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:works.bepress.com:houbing_song-1506","is_oa":false,"landing_page_url":"https://works.bepress.com/houbing_song/391","pdf_url":null,"source":{"id":"https://openalex.org/S4377196356","display_name":"Scholarly Commons (Embry\u2013Riddle Aeronautical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I84475105","host_organization_name":"Embry\u2013Riddle Aeronautical University","host_organization_lineage":["https://openalex.org/I84475105"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Houbing Song","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310348","display_name":"Embry-Riddle Aeronautical University","ror":"https://ror.org/010jskt71"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W193545200","https://openalex.org/W1502375784","https://openalex.org/W1576445103","https://openalex.org/W1896424170","https://openalex.org/W1919803322","https://openalex.org/W1985697265","https://openalex.org/W2113362355","https://openalex.org/W2140494000","https://openalex.org/W2149933564","https://openalex.org/W2163605009","https://openalex.org/W2164943005","https://openalex.org/W2165698076","https://openalex.org/W2253429366","https://openalex.org/W2293066912","https://openalex.org/W2577325523","https://openalex.org/W2582160025","https://openalex.org/W2591924527","https://openalex.org/W2592230399","https://openalex.org/W2603831296","https://openalex.org/W2604474127","https://openalex.org/W2613274667","https://openalex.org/W2772465720","https://openalex.org/W2945183595","https://openalex.org/W2963578416","https://openalex.org/W2964278684","https://openalex.org/W2964288524","https://openalex.org/W2964317695","https://openalex.org/W3033901474","https://openalex.org/W3036653218","https://openalex.org/W3080499510","https://openalex.org/W3085604187","https://openalex.org/W3103998378","https://openalex.org/W3127906832","https://openalex.org/W4255815195","https://openalex.org/W4289810945","https://openalex.org/W4293659930","https://openalex.org/W4299518610","https://openalex.org/W6607789936","https://openalex.org/W6634343353","https://openalex.org/W6639495226","https://openalex.org/W6676514679","https://openalex.org/W6682132143","https://openalex.org/W6684191040","https://openalex.org/W6713955831","https://openalex.org/W6732850681","https://openalex.org/W6746963075","https://openalex.org/W6767044186"],"related_works":["https://openalex.org/W4309131238","https://openalex.org/W4380355000","https://openalex.org/W3176438653","https://openalex.org/W2981628807","https://openalex.org/W4379875147","https://openalex.org/W3012393889","https://openalex.org/W3189091156","https://openalex.org/W3014041368","https://openalex.org/W4386087993","https://openalex.org/W4285815841"],"abstract_inverted_index":{"In":[0,143],"this":[1,118,144,216],"paper,":[2],"we":[3,146,179],"study":[4],"a":[5,76,97,106,148,159,185,204,209],"not":[6],"well-investigated":[7],"but":[8],"important":[9],"transfer":[10,29,111,153,255],"learning":[11,30,154],"problem":[12],"termed":[13],"Distant":[14,181],"Domain":[15],"Transfer":[16],"Learning":[17],"(DDTL).":[18],"This":[19],"topic":[20],"is":[21],"closely":[22],"related":[23],"to":[24,47,52,115,190,239,249],"negative":[25],"transfer.":[26],"Unlike":[27],"conventional":[28],"problems":[31],"which":[32,156],"assume":[33],"that":[34],"the":[35,39,58,61,88,92,213],"source":[36,89,168],"domain":[37,41,94,110,123,152,205,224],"and":[38,85,91,134,166,197,228,247],"target":[40,93,164],"are":[42,63,74,131,136],"more":[43],"or":[44,60],"less":[45],"similar":[46],"each":[48],"other,":[49],"DDTL":[50],"aims":[51],"make":[53],"efficient":[54],"transfers":[55],"even":[56],"when":[57],"domains":[59,196],"tasks":[62,198],"completely":[64,171],"different.":[65],"As":[66,212],"an":[67],"extreme":[68],"example":[69],"in":[70,87],"image":[71,124,226,230],"classification,":[72],"there":[73],"only":[75,95,137,158],"sufficient":[77],"amount":[78],"of":[79,82,100,120,162,175,215],"unlabeled":[80,167],"images":[81],"watches,":[83],"airplanes,":[84],"horses":[86],"domain,":[90],"has":[96,236],"small":[98],"set":[99,161],"labeled":[101,163],"human":[102],"face":[103],"images.":[104],"Previously,":[105],"few":[107],"instance-based":[108],"distant":[109,122,151,195,223,254],"algorithms":[112,130],"were":[113],"proposed":[114],"deal":[116],"with":[117,203],"type":[119],"binary":[121,140,229],"classification":[125,141,227,231,242],"problems.":[126,232],"Yet":[127],"most":[128],"existing":[129,253],"very":[132],"task-specific":[133],"they":[135],"good":[138],"at":[139],"tasks.":[142],"study,":[145,217],"propose":[147],"novel":[149,186],"feature-based":[150],"algorithm,":[155],"requires":[157],"tiny":[160],"data":[165,169],"from":[170],"different":[172],"domains.":[173],"Instead":[174],"selecting":[176],"intermediate":[177],"instances,":[178],"introduced":[180],"Feature":[182],"Fusion":[183],"(DFF),":[184],"feature":[187,210],"selection":[188],"method,":[189],"discover":[191],"general":[192],"features":[193],"cross":[194],"by":[199],"using":[200],"convolutional":[201],"autoencoder":[202],"distance":[206],"measurement":[207],"as":[208],"extractor.":[211],"novelty":[214],"it":[218,235],"can":[219],"effectively":[220],"handle":[221],"both":[222],"mutil-class":[225],"More":[233],"importantly,":[234],"achieved":[237],"up":[238,248],"19%":[240],"higher":[241,251],"accuracy":[243],"than":[244,252],"\"non-transfer\"":[245],"algorithms,":[246],"9%":[250],"algorithms.":[256]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}