{"id":"https://openalex.org/W2119591322","doi":"https://doi.org/10.1109/cit.2008.4594652","title":"Dimension reduction using least squares regression in multi-labeled text categorization","display_name":"Dimension reduction using least squares regression in multi-labeled text categorization","publication_year":2008,"publication_date":"2008-07-01","ids":{"openalex":"https://openalex.org/W2119591322","doi":"https://doi.org/10.1109/cit.2008.4594652","mag":"2119591322"},"language":"en","primary_location":{"id":"doi:10.1109/cit.2008.4594652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cit.2008.4594652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 8th IEEE International Conference on Computer and Information Technology","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/A5028761737","display_name":"Cheong Hee Park","orcid":"https://orcid.org/0000-0002-8233-2206"},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Cheong Hee Park","raw_affiliation_strings":["Department of Computer Science and Engineering, Chungnam National University, Daejeon, South Korea","Dept. of Comput. Sci. & Eng., ChungNam Nat. Univ., DaeJeon"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chungnam National University, Daejeon, South Korea","institution_ids":["https://openalex.org/I196345858"]},{"raw_affiliation_string":"Dept. of Comput. Sci. & Eng., ChungNam Nat. Univ., DaeJeon","institution_ids":["https://openalex.org/I196345858"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5028761737"],"corresponding_institution_ids":["https://openalex.org/I196345858"],"apc_list":null,"apc_paid":null,"fwci":1.1441,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.84544662,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"5","issue":null,"first_page":"71","last_page":"76"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9991000294685364,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9991000294685364,"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":0.9979000091552734,"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":0.9912999868392944,"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":0.7980526685714722},{"id":"https://openalex.org/keywords/sufficient-dimension-reduction","display_name":"Sufficient dimension reduction","score":0.7041766047477722},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6375893950462341},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.633491039276123},{"id":"https://openalex.org/keywords/sliced-inverse-regression","display_name":"Sliced inverse regression","score":0.6158270239830017},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6127471923828125},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5934479832649231},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5611880421638489},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5445561408996582},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4958706796169281},{"id":"https://openalex.org/keywords/optimal-discriminant-analysis","display_name":"Optimal discriminant analysis","score":0.4251607060432434},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4128100872039795},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4047073423862457},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.09021911025047302}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7980526685714722},{"id":"https://openalex.org/C27931671","wikidata":"https://www.wikidata.org/wiki/Q7634497","display_name":"Sufficient dimension reduction","level":3,"score":0.7041766047477722},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6375893950462341},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.633491039276123},{"id":"https://openalex.org/C41341539","wikidata":"https://www.wikidata.org/wiki/Q7540242","display_name":"Sliced inverse regression","level":3,"score":0.6158270239830017},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6127471923828125},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5934479832649231},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5611880421638489},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5445561408996582},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4958706796169281},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.4251607060432434},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4128100872039795},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4047073423862457},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.09021911025047302},{"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/cit.2008.4594652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cit.2008.4594652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 8th IEEE International Conference on Computer and Information Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W101693742","https://openalex.org/W1554944419","https://openalex.org/W1979974203","https://openalex.org/W2097616835","https://openalex.org/W2118712128","https://openalex.org/W2119466907","https://openalex.org/W2121647436","https://openalex.org/W2133199783","https://openalex.org/W2135346934","https://openalex.org/W2139212933","https://openalex.org/W2146012283","https://openalex.org/W2150102617","https://openalex.org/W2172000360","https://openalex.org/W2799061466","https://openalex.org/W4230946174","https://openalex.org/W6604126237","https://openalex.org/W6677758222","https://openalex.org/W6681875376"],"related_works":["https://openalex.org/W2811379001","https://openalex.org/W2342269520","https://openalex.org/W1976207164","https://openalex.org/W2080981338","https://openalex.org/W179183578","https://openalex.org/W2255588376","https://openalex.org/W2567488862","https://openalex.org/W2098495410","https://openalex.org/W2565236036","https://openalex.org/W2747414702"],"abstract_inverted_index":{"Dimension":[0],"reduction":[1,19,27,153],"is":[2],"a":[3,110,122,130],"preprocessing":[4],"step":[5],"by":[6],"which":[7,134],"small":[8],"number":[9],"of":[10,52,60,73],"optimal":[11,127],"features":[12,98,128],"are":[13,67,135,147],"extracted.":[14],"Among":[15],"several":[16],"statistical":[17],"dimension":[18,26,152],"methods,":[20],"Linear":[21],"discriminant":[22],"analysis":[23],"(LDA)":[24],"performs":[25],"to":[28,44,95],"maximize":[29],"class":[30],"separability":[31],"in":[32,38,69,82,129,144],"the":[33,50,53,58,61,70,86],"reduced":[34],"dimensional":[35,101,132],"space.":[36],"However,":[37],"multi-labeled":[39,83,123],"problems,":[40],"data":[41],"samples":[42],"belonging":[43],"multiple":[45,74,89,117],"classes":[46,56],"cause":[47],"contradiction":[48],"between":[49,55],"maximization":[51],"distances":[54],"and":[57,109,125,155],"minimization":[59],"scatter":[62],"within":[63],"classes,":[64],"since":[65],"they":[66],"placed":[68],"overlapping":[71],"area":[72],"classes.":[75],"In":[76],"this":[77],"paper,":[78],"we":[79,104],"show":[80],"that":[81],"text":[84,145],"categorization,":[85],"outputs":[87],"from":[88,121],"linear":[90,111],"methods":[91,154],"can":[92],"be":[93],"used":[94],"compose":[96],"new":[97],"for":[99,116],"low":[100,131],"representation.":[102],"Especially,":[103],"apply":[105],"least":[106],"squares":[107],"regression":[108],"support":[112],"vector":[113],"machine":[114],"(SVM)":[115],"binary-class":[118],"problems":[119],"constructed":[120],"problem":[124],"obtain":[126],"space":[133],"fed":[136],"into":[137],"another":[138],"classification":[139,157],"algorithm.":[140],"Extensive":[141],"experimental":[142],"results":[143],"categorization":[146],"presented":[148],"comparing":[149],"with":[150],"other":[151],"multi-label":[156],"algorithms.":[158]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}