{"id":"https://openalex.org/W2601798039","doi":"https://doi.org/10.1109/taai.2016.7880182","title":"Semi-supervised sufficient dimension reduction under class-prior change","display_name":"Semi-supervised sufficient dimension reduction under class-prior change","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2601798039","doi":"https://doi.org/10.1109/taai.2016.7880182","mag":"2601798039"},"language":"en","primary_location":{"id":"doi:10.1109/taai.2016.7880182","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taai.2016.7880182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5057294791","display_name":"Hideko Kawakubo","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideko Kawakubo","raw_affiliation_strings":["Department of Computer Science, Tokyo Institute of Technology, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072744508","display_name":"Masashi Sugiyama","orcid":"https://orcid.org/0000-0001-6658-6743"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masashi Sugiyama","raw_affiliation_strings":["Department of Computer Science, Tokyo Institute of Technology, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":null,"first_page":"146","last_page":"153"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9980999827384949,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9966999888420105,"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/T10057","display_name":"Face and Expression Recognition","score":0.9940999746322632,"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/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.8696290254592896},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.786988377571106},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.7408371567726135},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.7233401536941528},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6873422861099243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6242861151695251},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5609548091888428},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5033776164054871},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4788103699684143},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.46413132548332214},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42144671082496643},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4036552309989929},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22997653484344482},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1385425627231598}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.8696290254592896},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.786988377571106},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.7408371567726135},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.7233401536941528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6873422861099243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6242861151695251},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5609548091888428},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5033776164054871},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4788103699684143},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.46413132548332214},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42144671082496643},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4036552309989929},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22997653484344482},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1385425627231598},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taai.2016.7880182","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taai.2016.7880182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320912","display_name":"Ministry of Education, Culture, Sports, Science and Technology","ror":"https://ror.org/048rj2z13"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W189742998","https://openalex.org/W1551909886","https://openalex.org/W1855110042","https://openalex.org/W1978930294","https://openalex.org/W1997384244","https://openalex.org/W2013974460","https://openalex.org/W2036163871","https://openalex.org/W2072460512","https://openalex.org/W2083252561","https://openalex.org/W2087717467","https://openalex.org/W2117381306","https://openalex.org/W2118543192","https://openalex.org/W2141253686","https://openalex.org/W2144405862","https://openalex.org/W2148603752","https://openalex.org/W2153635508","https://openalex.org/W2157801062","https://openalex.org/W2158040987","https://openalex.org/W2162651021","https://openalex.org/W2163490846","https://openalex.org/W2171050905","https://openalex.org/W2295409730","https://openalex.org/W2306907554","https://openalex.org/W2616198738","https://openalex.org/W2969813877","https://openalex.org/W3098474648","https://openalex.org/W3120421331","https://openalex.org/W4244473079","https://openalex.org/W6607672814","https://openalex.org/W6633119224","https://openalex.org/W6682834636","https://openalex.org/W6683083057","https://openalex.org/W6698112956","https://openalex.org/W7071374342"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241","https://openalex.org/W2354420595","https://openalex.org/W2168489430"],"abstract_inverted_index":{"Sufficient":[0],"dimension":[1,10,93],"reduction":[2,94],"(SDR)":[3],"is":[4,25,40,77],"a":[5,58],"popular":[6],"framework":[7],"for":[8,92],"supervised":[9,35],"reduction,":[11],"aiming":[12],"at":[13],"reducing":[14],"the":[15,29,43,52,74,85,106,126],"dimensionality":[16],"of":[17,45,128],"input":[18],"data":[19,24,76,113],"while":[20],"information":[21],"on":[22],"output":[23],"maximally":[26],"maintained.":[27],"On":[28],"other":[30],"hand,":[31],"in":[32,47,116],"many":[33],"recent":[34],"classification":[36],"learning":[37,108],"tasks,":[38],"it":[39],"conceivable":[41],"that":[42],"balance":[44],"samples":[46],"each":[48],"class":[49],"varies":[50],"between":[51],"training":[53,75,120],"and":[54],"testing":[55],"phases.":[56],"Such":[57],"phenomenon,":[59],"referred":[60],"to":[61,69,96,99,118],"as":[62],"class-prior":[63,103],"change,":[64],"causes":[65],"existing":[66],"SDR":[67,87],"methods":[68],"perform":[70],"undesirably":[71],"particularly":[72],"when":[73],"highly":[78],"imbalanced.":[79],"In":[80],"this":[81],"paper,":[82],"we":[83,124],"extend":[84],"state-of-the-art":[86],"method":[88],"called":[89],"leastsquares":[90],"gradients":[91],"(LSGDR)":[95],"be":[97],"able":[98],"cope":[100],"with":[101],"such":[102],"change":[104],"under":[105],"semi-supervised":[107],"setup":[109],"where":[110],"unlabeled":[111],"test":[112],"are":[114],"available":[115],"addition":[117],"labeled":[119],"data.":[121],"Through":[122],"experiments,":[123],"demonstrate":[125],"usefulness":[127],"our":[129],"proposed":[130],"method.":[131]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
