{"id":"https://openalex.org/W2001499070","doi":"https://doi.org/10.4304/jcp.9.3.576-580","title":"An Efficient Dimensionality Reduction Approach for Small-sample Size and High-dimensional Data Modeling","display_name":"An Efficient Dimensionality Reduction Approach for Small-sample Size and High-dimensional Data Modeling","publication_year":2014,"publication_date":"2014-03-01","ids":{"openalex":"https://openalex.org/W2001499070","doi":"https://doi.org/10.4304/jcp.9.3.576-580","mag":"2001499070"},"language":"en","primary_location":{"id":"doi:10.4304/jcp.9.3.576-580","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jcp.9.3.576-580","pdf_url":null,"source":{"id":"https://openalex.org/S77894049","display_name":"Journal of Computers","issn_l":"1796-203X","issn":["1796-203X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computers","raw_type":"journal-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/A5041427080","display_name":"Xintao Qiu","orcid":"https://orcid.org/0000-0002-8560-7017"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xintao Qiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016681542","display_name":"Dongmei Fu","orcid":"https://orcid.org/0000-0003-3918-9448"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongmei Fu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5045318031","display_name":"Zhenduo Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenduo Fu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4142,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.73482021,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"9","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9768999814987183,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9768999814987183,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9765999913215637,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9708999991416931,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7582069039344788},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.6530164480209351},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.6503813862800598},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.565466046333313},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5347038507461548},{"id":"https://openalex.org/keywords/data-reduction","display_name":"Data reduction","score":0.5314271450042725},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.45397213101387024},{"id":"https://openalex.org/keywords/dimensional-reduction","display_name":"Dimensional reduction","score":0.4304671883583069},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.30096542835235596},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27712172269821167},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24306806921958923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23635903000831604},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.08631548285484314},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.07192537188529968}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7582069039344788},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.6530164480209351},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.6503813862800598},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.565466046333313},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5347038507461548},{"id":"https://openalex.org/C153914771","wikidata":"https://www.wikidata.org/wiki/Q5227343","display_name":"Data reduction","level":2,"score":0.5314271450042725},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.45397213101387024},{"id":"https://openalex.org/C2780091087","wikidata":"https://www.wikidata.org/wiki/Q3092131","display_name":"Dimensional reduction","level":2,"score":0.4304671883583069},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30096542835235596},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27712172269821167},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24306806921958923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23635903000831604},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.08631548285484314},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.07192537188529968},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","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:10.4304/jcp.9.3.576-580","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jcp.9.3.576-580","pdf_url":null,"source":{"id":"https://openalex.org/S77894049","display_name":"Journal of Computers","issn_l":"1796-203X","issn":["1796-203X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6899999976158142,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1971979008","https://openalex.org/W1982628045","https://openalex.org/W1991130394","https://openalex.org/W1993756242","https://openalex.org/W2011580004","https://openalex.org/W2024165284","https://openalex.org/W2024182011","https://openalex.org/W2026535637","https://openalex.org/W2037271374","https://openalex.org/W2039002209","https://openalex.org/W2039562180","https://openalex.org/W2077964202","https://openalex.org/W2098057602","https://openalex.org/W2100235303","https://openalex.org/W2100383651","https://openalex.org/W2103537693","https://openalex.org/W2104290444","https://openalex.org/W2126562020","https://openalex.org/W2129676447","https://openalex.org/W2136124479","https://openalex.org/W2141200867","https://openalex.org/W2143391154","https://openalex.org/W2143426320","https://openalex.org/W2153635508","https://openalex.org/W2997701990","https://openalex.org/W6648707223","https://openalex.org/W6670150809","https://openalex.org/W6675747103"],"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/W2170142494"],"abstract_inverted_index":{"As":[0],"for":[1,103,162],"massive":[2],"multidimensional":[3],"data":[4,30,72,83,108,145,157],"are":[5],"being":[6],"generated":[7],"in":[8],"a":[9,143],"wide":[10],"range":[11],"of":[12,21,80,109,136],"emerging":[13],"applications,":[14],"this":[15,140,163],"paper":[16,141],"introduces":[17],"two":[18],"new":[19],"methods":[20,94],"dimension":[22],"reduction":[23,147],"to":[24,50,63,99],"conduct":[25],"small-sample":[26],"size":[27],"and":[28,32,41,53,91,105,133,159],"high-dimensional":[29,154],"processing":[31],"modeling.":[33],"Through":[34],"combining":[35],"the":[36,56,64,71,86,100,107,118,128,134,153],"support":[37],"vector":[38],"machine":[39],"(SVM)":[40],"recursive":[42],"feature":[43,65,89,92,113,120,124],"elimination":[44],"(RFE),":[45],"SVM-RFE":[46],"algorithm":[47],"is":[48,122],"proposed":[49,87],"select":[51],"features,":[52],"further,":[54],"adding":[55],"higher":[57],"order":[58,75],"singular":[59],"value":[60],"decomposition":[61],"(HOSVD)":[62],"extraction":[66,93,125],"which":[67],"involves":[68],"successfully":[69],"organizing":[70],"into":[73],"high":[74],"tensor":[76],"pattern.":[77],"The":[78,112],"validation":[79],"simulation":[81],"experiment":[82],"shows":[84],"that":[85,117,149],"novel":[88],"selection":[90,114],"can":[95,150],"be":[96],"effectively":[97,151],"applied":[98],"research":[101],"work":[102],"analyzing":[104],"modeling":[106],"atmospheric":[110],"corrosion.":[111],"method":[115,126],"pledges":[116],"remaining":[119],"subset":[121],"optimal;":[123],"reserves":[127],"original":[129],"structure,":[130],"discriminate":[131],"information,":[132],"integrity":[135],"data,":[137],"etc.":[138],"Finally,":[139],"proposes":[142],"complete":[144],"dimensionality":[146],"solution":[148,164],"solve":[152],"small":[155],"sample":[156],"problem,":[158],"code":[160],"programming":[161],"has":[165],"been":[166],"implemented.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}