{"id":"https://openalex.org/W2025562014","doi":"https://doi.org/10.1145/2701126.2701204","title":"Feature extraction from covariance by using kernel method for classifying polysomnographys data","display_name":"Feature extraction from covariance by using kernel method for classifying polysomnographys data","publication_year":2015,"publication_date":"2015-01-08","ids":{"openalex":"https://openalex.org/W2025562014","doi":"https://doi.org/10.1145/2701126.2701204","mag":"2025562014"},"language":"en","primary_location":{"id":"doi:10.1145/2701126.2701204","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2701126.2701204","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication","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/A5073397037","display_name":"Hong Quy Nguyen","orcid":"https://orcid.org/0000-0003-0905-6011"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hong Quy Nguyen","raw_affiliation_strings":["Chonnam National University, Gwangju, South Korea","Chonnam National University , Gwangju, South Korea"],"affiliations":[{"raw_affiliation_string":"Chonnam National University, Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]},{"raw_affiliation_string":"Chonnam National University , Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087619194","display_name":"Hyung-Jeong Yang","orcid":"https://orcid.org/0000-0003-3024-5060"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyung-Jeong Yang","raw_affiliation_strings":["Chonnam National University, Gwangju, South Korea","Chonnam National University , Gwangju, South Korea"],"affiliations":[{"raw_affiliation_string":"Chonnam National University, Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]},{"raw_affiliation_string":"Chonnam National University , Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058671274","display_name":"Thao Nguyen Thieu","orcid":null},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Thao Nguyen Thieu","raw_affiliation_strings":["Chonnam National University, Gwangju, South Korea","Chonnam National University , Gwangju, South Korea"],"affiliations":[{"raw_affiliation_string":"Chonnam National University, Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]},{"raw_affiliation_string":"Chonnam National University , Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073397037"],"corresponding_institution_ids":["https://openalex.org/I111277659"],"apc_list":null,"apc_paid":null,"fwci":0.5744,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66552006,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9836000204086304,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9836000204086304,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10985","display_name":"Sleep and Wakefulness Research","score":0.9830999970436096,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9659000039100647,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.749894380569458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6007150411605835},{"id":"https://openalex.org/keywords/geodesic","display_name":"Geodesic","score":0.5987216830253601},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5915045738220215},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5727157592773438},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5148605704307556},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4818938374519348},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.47455817461013794},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44302451610565186},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43465813994407654},{"id":"https://openalex.org/keywords/tangent-space","display_name":"Tangent space","score":0.4222294092178345},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.38103625178337097},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33450835943222046},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.21678537130355835},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16111144423484802},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.081645667552948}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.749894380569458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6007150411605835},{"id":"https://openalex.org/C165818556","wikidata":"https://www.wikidata.org/wiki/Q213488","display_name":"Geodesic","level":2,"score":0.5987216830253601},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5915045738220215},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5727157592773438},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5148605704307556},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4818938374519348},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.47455817461013794},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44302451610565186},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43465813994407654},{"id":"https://openalex.org/C157157409","wikidata":"https://www.wikidata.org/wiki/Q909601","display_name":"Tangent space","level":2,"score":0.4222294092178345},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38103625178337097},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33450835943222046},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.21678537130355835},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16111144423484802},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.081645667552948},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2701126.2701204","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2701126.2701204","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1545241942","https://openalex.org/W1549083695","https://openalex.org/W1575153708","https://openalex.org/W1608735834","https://openalex.org/W1983104971","https://openalex.org/W1983496390","https://openalex.org/W1993014362","https://openalex.org/W2059640215","https://openalex.org/W2096597330","https://openalex.org/W2109409043","https://openalex.org/W2109531142","https://openalex.org/W2116022929","https://openalex.org/W2147757279","https://openalex.org/W2162800060","https://openalex.org/W2163462953","https://openalex.org/W2167164761","https://openalex.org/W2441535889","https://openalex.org/W2541652489","https://openalex.org/W3014771378","https://openalex.org/W3022302886","https://openalex.org/W3038420293","https://openalex.org/W3100876745","https://openalex.org/W4206033904"],"related_works":["https://openalex.org/W1741548419","https://openalex.org/W4317607601","https://openalex.org/W2056283567","https://openalex.org/W1565299197","https://openalex.org/W2088032561","https://openalex.org/W2542234468","https://openalex.org/W1585398011","https://openalex.org/W2544132760","https://openalex.org/W206259832","https://openalex.org/W2892571874"],"abstract_inverted_index":{"Using":[0],"covariance":[1,20,94,159],"as":[2,72,113,161],"natural":[3],"features":[4,139],"for":[5,42,136],"Classifying":[6],"Polysomnographys":[7],"signal":[8],"was":[9],"not":[10],"new":[11],"in":[12,83],"the":[13,16,25,77,125,128],"literature.":[14],"In":[15,46],"literature,":[17],"space":[18,44,99],"of":[19,100,117,130],"is":[21,29,62,90,155],"an":[22],"abstract":[23],"space,":[24],"Riemannian":[26],"manifold.":[27,84],"It":[28],"non-trivial":[30],"to":[31,48,54,91,96,124,157,178],"classify":[32,55],"by":[33,66,140],"using":[34,67],"well-known":[35],"classifier":[36,70],"because":[37],"all":[38,93],"algorithms":[39],"were":[40],"designed":[41],"Euclidean":[43],"only.":[45],"order":[47],"overcome,":[49],"there":[50],"are":[51],"two":[52,81],"ways":[53],"with":[56,76],"this":[57],"feature.":[58],"First":[59],"way,":[60,87],"it":[61],"possible":[63,156],"apply":[64,109],"directly":[65],"based":[68,180],"distance":[69,79,179],"such":[71],"k-nearest":[73],"neighborhoods":[74],"(k-NN)":[75],"geodesic":[78],"between":[80],"points":[82],"The":[85],"second":[86],"indirect":[88,182],"method":[89,112,135,167],"transform":[92],"data":[95],"approximated":[97],"tangent":[98],"a":[101,110,134,142,174],"specific":[102],"symmetric":[103],"and":[104,108,127,172,181],"positive":[105],"definite":[106],"matrix":[107],"classification":[111],"usual":[114],"way.":[115],"All":[116],"these":[118],"methods,":[119],"themselves":[120],"have":[121],"drawback":[122],"due":[123],"distortion":[126],"geometry":[129],"data.":[131],"We":[132,164],"introduced":[133],"extracting":[137],"latent":[138],"combining":[141],"kernel":[143],"method.":[144,183],"Thus,":[145],"we":[146],"used":[147],"Kernel":[148],"Local":[149],"Fisher":[150],"Discriminant":[151],"Analysis":[152],"(KLFDA)":[153],"which":[154],"take":[158],"matrices":[160],"its":[162],"inputs.":[163],"evaluated":[165],"our":[166],"on":[168],"Sleep":[169],"Stages":[170],"Dataset":[171],"archived":[173],"better":[175],"result":[176],"compared":[177]},"counts_by_year":[{"year":2017,"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"}