{"id":"https://openalex.org/W4380032302","doi":"https://doi.org/10.1109/tim.2023.3284929","title":"A Novel Nonconvex Low-Rank Tensor Completion Approach for Traffic Sensor Data Recovery From Incomplete Measurements","display_name":"A Novel Nonconvex Low-Rank Tensor Completion Approach for Traffic Sensor Data Recovery From Incomplete Measurements","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4380032302","doi":"https://doi.org/10.1109/tim.2023.3284929"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3284929","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3284929","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","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/A5100333930","display_name":"Xiaobo Chen","orcid":"https://orcid.org/0000-0001-9940-1637"},"institutions":[{"id":"https://openalex.org/I83776822","display_name":"Shandong Institute of Business and Technology","ror":"https://ror.org/03rrkrc24","country_code":"CN","type":"education","lineage":["https://openalex.org/I83776822"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaobo Chen","raw_affiliation_strings":["School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China","Jiangsu Key Lab of Image and Video Understanding for Social Security, Nanjing, China","School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China","institution_ids":["https://openalex.org/I83776822"]},{"raw_affiliation_string":"Jiangsu Key Lab of Image and Video Understanding for Social Security, Nanjing, China","institution_ids":[]},{"raw_affiliation_string":"School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China","institution_ids":["https://openalex.org/I83776822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112933138","display_name":"Kaiyuan Wang","orcid":"https://orcid.org/0000-0001-9201-8977"},"institutions":[{"id":"https://openalex.org/I83776822","display_name":"Shandong Institute of Business and Technology","ror":"https://ror.org/03rrkrc24","country_code":"CN","type":"education","lineage":["https://openalex.org/I83776822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiyuan Wang","raw_affiliation_strings":["School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China","School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China","institution_ids":["https://openalex.org/I83776822"]},{"raw_affiliation_string":"School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China","institution_ids":["https://openalex.org/I83776822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091320821","display_name":"Zuoyong Li","orcid":"https://orcid.org/0000-0003-0952-9915"},"institutions":[{"id":"https://openalex.org/I354108","display_name":"Minjiang University","ror":"https://ror.org/00s7tkw17","country_code":"CN","type":"education","lineage":["https://openalex.org/I354108"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zuoyong Li","raw_affiliation_strings":["Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China","College of Computer and Control Engineering, Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China","institution_ids":["https://openalex.org/I354108"]},{"raw_affiliation_string":"College of Computer and Control Engineering, Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China","institution_ids":["https://openalex.org/I354108"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100433744","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0003-4087-6544"},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["Department of Bioengineering, Lehigh University, Bethlehem, PA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Bioengineering, Lehigh University, Bethlehem, PA, USA","institution_ids":["https://openalex.org/I186143895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039671101","display_name":"Qiaolin Ye","orcid":"https://orcid.org/0000-0002-8793-8610"},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiaolin Ye","raw_affiliation_strings":["College of Information Science and Technology, Nanjing Forestry University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100333930"],"corresponding_institution_ids":["https://openalex.org/I83776822"],"apc_list":null,"apc_paid":null,"fwci":1.579,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.85272442,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9987999796867371,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.68790602684021},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6519120931625366},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5031704306602478},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5021364688873291},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.46191835403442383},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.45431825518608093},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4312613606452942},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41121432185173035},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30280205607414246},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22262221574783325},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19296050071716309}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.68790602684021},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6519120931625366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5031704306602478},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5021364688873291},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.46191835403442383},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.45431825518608093},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4312613606452942},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41121432185173035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30280205607414246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22262221574783325},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19296050071716309},{"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3284929","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3284929","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5336214237","display_name":null,"funder_award_id":"61773184","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6059588814","display_name":null,"funder_award_id":"2022XDRH014","funder_id":"https://openalex.org/F4320327070","funder_display_name":"Yantai Science and Technology Bureau"},{"id":"https://openalex.org/G8600505247","display_name":null,"funder_award_id":"2018YFB2100500","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327070","display_name":"Yantai Science and Technology Bureau","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W195665135","https://openalex.org/W2014237985","https://openalex.org/W2036708807","https://openalex.org/W2049633694","https://openalex.org/W2090035883","https://openalex.org/W2091449379","https://openalex.org/W2114933215","https://openalex.org/W2120718561","https://openalex.org/W2122825543","https://openalex.org/W2130187411","https://openalex.org/W2146842127","https://openalex.org/W2153919224","https://openalex.org/W2163150789","https://openalex.org/W2164278908","https://openalex.org/W2187214659","https://openalex.org/W2192203593","https://openalex.org/W2529827714","https://openalex.org/W2594376584","https://openalex.org/W2616654950","https://openalex.org/W2726858467","https://openalex.org/W2799891732","https://openalex.org/W2800435428","https://openalex.org/W2884159946","https://openalex.org/W2963474950","https://openalex.org/W2991063736","https://openalex.org/W3006180391","https://openalex.org/W3014706756","https://openalex.org/W3022125780","https://openalex.org/W3034951560","https://openalex.org/W3100203369","https://openalex.org/W3121013072","https://openalex.org/W3123710172","https://openalex.org/W3137234345","https://openalex.org/W3166291381","https://openalex.org/W3169524537","https://openalex.org/W3170107305","https://openalex.org/W3170571485","https://openalex.org/W3188900617","https://openalex.org/W3198381955","https://openalex.org/W3201047833","https://openalex.org/W3202635391","https://openalex.org/W3212797624","https://openalex.org/W3216566650","https://openalex.org/W4224271671","https://openalex.org/W4225589992","https://openalex.org/W4229706427","https://openalex.org/W4281257454","https://openalex.org/W4285179601","https://openalex.org/W4285279264","https://openalex.org/W4285307478","https://openalex.org/W4285740942","https://openalex.org/W4324116476","https://openalex.org/W6607912541","https://openalex.org/W6713783767","https://openalex.org/W6804055087"],"related_works":["https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4380150146","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W1977098485","https://openalex.org/W4285201053","https://openalex.org/W4256612600"],"abstract_inverted_index":{"Complete":[0],"traffic":[1,20,78,103,130,157,228],"sensor":[2,136],"data":[3,33,63,131,158],"is":[4,94,193,214],"considered":[5],"to":[6,38,96,120,195,200],"be":[7],"one":[8],"of":[9,72,77,91,102,129,156,162,177,181,186,205,238],"the":[10,19,25,40,45,73,82,98,123,126,134,140,153,160,175,178,182,202,206,236],"critical":[11],"ingredients":[12],"for":[13],"intelligent":[14],"transportation":[15],"systems":[16],"(ITS).":[17],"However,":[18],"measurements":[21],"prevalently":[22],"suffer":[23],"from":[24,159],"inevitable":[26],"and":[27,44,113,125,167,170,223,245],"ubiquitous":[28],"missing":[29,32,243,246],"values.":[30],"Current":[31],"completion":[34,65],"algorithms":[35],"are":[36,230],"difficult":[37],"leverage":[39],"global":[41,99,164],"low-rank":[42,61],"property":[43],"fine-grained":[46,168],"spatial-temporal":[47,62,75],"structure":[48,155],"simultaneously.":[49],"To":[50],"circumvent":[51],"this":[52,54],"problem,":[53],"work":[55],"presents":[56],"a":[57,144,208],"novel":[58],"collaborative":[59],"nonconvex":[60,89,146],"tensor":[64,83],"model":[66,117,240],"that":[67],"can":[68,151],"take":[69],"full":[70],"advantage":[71],"inherent":[74,154],"characteristics":[76],"measurement":[79],"data.":[80,104],"First,":[81],"Schatten":[84],"p-norm,":[85],"as":[86],"an":[87,108,115,189],"effective":[88],"surrogate":[90],"rank":[92],"function,":[93],"used":[95],"exploit":[97],"multi-dimensional":[100,165],"correlation":[101,166],"Then,":[105,173],"we":[106],"present":[107],"elastic":[109],"net":[110],"self-representation":[111],"method":[112,150,185],"utilize":[114],"autoregression":[116],"in":[118,133,143,174],"order":[119],"simultaneously":[121],"capture":[122],"self-similarity":[124],"temporal":[127,171],"continuity":[128],"acquired":[132],"same":[135],"network.":[137],"By":[138],"integrating":[139],"above":[141],"elements":[142],"unified":[145],"learning":[147],"model,":[148,207],"our":[149,197,239],"explore":[152],"viewpoints":[161],"both":[163],"spatial":[169],"dependency.":[172],"view":[176],"general":[179],"framework":[180],"alternating":[183],"directions":[184],"multipliers":[187],"(ADMM),":[188],"efficient":[190],"iterative":[191],"algorithm":[192,213],"designed":[194],"solve":[196],"model.":[198],"Besides,":[199],"optimize":[201],"parameter":[203,211,219],"combination":[204],"Bayesian":[209],"optimization-based":[210],"selection":[212],"developed,":[215],"which":[216],"avoids":[217],"manual":[218],"adjustment.":[220],"Extensive":[221],"experiments":[222],"analyses":[224],"on":[225],"two":[226],"real-world":[227],"datasets":[229],"carried":[231],"out.":[232],"The":[233],"results":[234],"demonstrate":[235],"advantages":[237],"under":[241],"diverse":[242],"patterns":[244],"ratios.":[247]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}