{"id":"https://openalex.org/W3217396774","doi":"https://doi.org/10.1109/icce-tw52618.2021.9603077","title":"Graph Signal Denoising Method Using the K-Nearest Neighbors Found by Dijkstra's Algorithm","display_name":"Graph Signal Denoising Method Using the K-Nearest Neighbors Found by Dijkstra's Algorithm","publication_year":2021,"publication_date":"2021-09-15","ids":{"openalex":"https://openalex.org/W3217396774","doi":"https://doi.org/10.1109/icce-tw52618.2021.9603077","mag":"3217396774"},"language":"en","primary_location":{"id":"doi:10.1109/icce-tw52618.2021.9603077","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw52618.2021.9603077","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","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/A5085387617","display_name":"Chien\u2010Cheng Tseng","orcid":"https://orcid.org/0000-0002-4235-8567"},"institutions":[{"id":"https://openalex.org/I4387154394","display_name":"National Kaohsiung University of Science and Technology","ror":"https://ror.org/00hfj7g70","country_code":null,"type":"education","lineage":["https://openalex.org/I4387154394"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Chien-Cheng Tseng","raw_affiliation_strings":["National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I4387154394","https://openalex.org/I4387154394"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055631887","display_name":"Su\u2010Ling Lee","orcid":"https://orcid.org/0000-0003-1043-7866"},"institutions":[{"id":"https://openalex.org/I80327900","display_name":"Chang Jung Christian University","ror":"https://ror.org/02s3d7j94","country_code":"TW","type":"education","lineage":["https://openalex.org/I80327900"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Su-Ling Lee","raw_affiliation_strings":["Chang-Jung Christian University, Tainan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Chang-Jung Christian University, Tainan, Taiwan","institution_ids":["https://openalex.org/I80327900"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085387617"],"corresponding_institution_ids":["https://openalex.org/I4387154394"],"apc_list":null,"apc_paid":null,"fwci":0.2719,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64744425,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T11273","display_name":"Advanced Graph Neural Networks","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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9699000120162964,"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/adjacency-matrix","display_name":"Adjacency matrix","score":0.8239144086837769},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.7243669033050537},{"id":"https://openalex.org/keywords/graph-energy","display_name":"Graph energy","score":0.6269888877868652},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5584455132484436},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5518909692764282},{"id":"https://openalex.org/keywords/dijkstras-algorithm","display_name":"Dijkstra's algorithm","score":0.5177366137504578},{"id":"https://openalex.org/keywords/degree-matrix","display_name":"Degree matrix","score":0.5013737678527832},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4585759937763214},{"id":"https://openalex.org/keywords/regular-graph","display_name":"Regular graph","score":0.44893157482147217},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.43134650588035583},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4171414077281952},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.41597577929496765},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39073824882507324},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.2926742434501648},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.2897266149520874},{"id":"https://openalex.org/keywords/graph-power","display_name":"Graph power","score":0.2411850392818451},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.2319737672805786},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18940892815589905},{"id":"https://openalex.org/keywords/shortest-path-problem","display_name":"Shortest path problem","score":0.1684868037700653}],"concepts":[{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.8239144086837769},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.7243669033050537},{"id":"https://openalex.org/C78913703","wikidata":"https://www.wikidata.org/wiki/Q5597087","display_name":"Graph energy","level":5,"score":0.6269888877868652},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5584455132484436},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5518909692764282},{"id":"https://openalex.org/C173870130","wikidata":"https://www.wikidata.org/wiki/Q8548","display_name":"Dijkstra's algorithm","level":4,"score":0.5177366137504578},{"id":"https://openalex.org/C162199024","wikidata":"https://www.wikidata.org/wiki/Q3085391","display_name":"Degree matrix","level":5,"score":0.5013737678527832},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4585759937763214},{"id":"https://openalex.org/C123482549","wikidata":"https://www.wikidata.org/wiki/Q826467","display_name":"Regular graph","level":5,"score":0.44893157482147217},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.43134650588035583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4171414077281952},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.41597577929496765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39073824882507324},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.2926742434501648},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2897266149520874},{"id":"https://openalex.org/C149530733","wikidata":"https://www.wikidata.org/wiki/Q5597091","display_name":"Graph power","level":4,"score":0.2411850392818451},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.2319737672805786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18940892815589905},{"id":"https://openalex.org/C22590252","wikidata":"https://www.wikidata.org/wiki/Q1058754","display_name":"Shortest path problem","level":3,"score":0.1684868037700653}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-tw52618.2021.9603077","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw52618.2021.9603077","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1485732691","https://openalex.org/W2525488343","https://openalex.org/W2766445337","https://openalex.org/W2938821438"],"related_works":["https://openalex.org/W2748456403","https://openalex.org/W3090939855","https://openalex.org/W3033749614","https://openalex.org/W3090739087","https://openalex.org/W3202178314","https://openalex.org/W4318995835","https://openalex.org/W2575552581","https://openalex.org/W2963150133","https://openalex.org/W3217396774","https://openalex.org/W2002653466"],"abstract_inverted_index":{"In":[0],"this":[1,32,68],"paper,":[2],"graph":[3,11,16,56,60,78,89],"signal":[4,12,90,95],"denoising":[5,13,121],"problem":[6],"is":[7,20,29,42,70,112,129],"investigated.":[8],"First,":[9],"conventional":[10,127],"method":[14,122,128],"using":[15,73],"Laplacian":[17,61],"matrix":[18,27,62],"(GLM)":[19],"described":[21],"to":[22,44,114],"show":[23,115],"that":[24],"a":[25,38,51,58,104],"big":[26],"inversion":[28],"needed":[30],"in":[31,54],"method.":[33,107],"To":[34],"reduce":[35],"computational":[36],"load,":[37],"modified":[39],"Dijkstra's":[40],"algorithm":[41],"presented":[43],"find":[45],"the":[46,55,65,74,84,93,97,116,119],"K-nearest":[47],"neighbors":[48],"(K-NN)":[49],"of":[50,64,88,118],"given":[52,98],"vertex":[53,69,99],"and":[57,77,91,123],"local":[59,85],"(LGLM)":[63],"sub-graph":[66],"around":[67],"constructed":[71],"by":[72,103],"K-NN":[75],"information":[76],"adjacency":[79],"matrix.":[80],"Then,":[81],"based":[82],"on":[83],"smoothness":[86],"property":[87],"LGLM,":[92],"denoised":[94],"at":[96],"can":[100],"be":[101],"computed":[102],"Cramer's":[105],"rule":[106],"Finally,":[108],"real":[109],"temperature":[110],"data":[111],"used":[113],"effectiveness":[117],"proposed":[120],"performance":[124],"comparison":[125],"with":[126],"made.":[130]},"counts_by_year":[{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
