{"id":"https://openalex.org/W3117280498","doi":"https://doi.org/10.1145/3417337","title":"Robust Tensor Recovery with Fiber Outliers for Traffic Events","display_name":"Robust Tensor Recovery with Fiber Outliers for Traffic Events","publication_year":2020,"publication_date":"2020-12-30","ids":{"openalex":"https://openalex.org/W3117280498","doi":"https://doi.org/10.1145/3417337","mag":"3117280498"},"language":"en","primary_location":{"id":"doi:10.1145/3417337","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3417337","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","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/A5101584498","display_name":"Yue Hu","orcid":"https://orcid.org/0000-0001-6579-0646"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yue Hu","raw_affiliation_strings":["Vanderbilt University, Nashville"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012556136","display_name":"Daniel B. Work","orcid":"https://orcid.org/0000-0003-0565-2158"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel B. Work","raw_affiliation_strings":["Vanderbilt University, Nashville"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville","institution_ids":["https://openalex.org/I200719446"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101584498"],"corresponding_institution_ids":["https://openalex.org/I200719446"],"apc_list":null,"apc_paid":null,"fwci":2.4779,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.8833272,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"15","issue":"1","first_page":"1","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9990000128746033,"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/T12303","display_name":"Tensor decomposition and applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9728999733924866,"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/outlier","display_name":"Outlier","score":0.7442793250083923},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.6542038321495056},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6236889362335205},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.6207370162010193},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5907842516899109},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5882993340492249},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5749238729476929},{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.5528745055198669},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.543563723564148},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4650883078575134},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4632417559623718},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34682342410087585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3235571086406708},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18576911091804504}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7442793250083923},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.6542038321495056},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6236889362335205},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.6207370162010193},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5907842516899109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5882993340492249},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5749238729476929},{"id":"https://openalex.org/C42704193","wikidata":"https://www.wikidata.org/wiki/Q7851097","display_name":"Tucker decomposition","level":4,"score":0.5528745055198669},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.543563723564148},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4650883078575134},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4632417559623718},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34682342410087585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3235571086406708},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18576911091804504},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3417337","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3417337","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W40609341","https://openalex.org/W1544613517","https://openalex.org/W1798398164","https://openalex.org/W1864134408","https://openalex.org/W1963826206","https://openalex.org/W1964965419","https://openalex.org/W1973943669","https://openalex.org/W1974476933","https://openalex.org/W1981392829","https://openalex.org/W1983357136","https://openalex.org/W1999136078","https://openalex.org/W2014211872","https://openalex.org/W2017464775","https://openalex.org/W2019569173","https://openalex.org/W2021002141","https://openalex.org/W2024165284","https://openalex.org/W2026493302","https://openalex.org/W2030928609","https://openalex.org/W2033903280","https://openalex.org/W2037271374","https://openalex.org/W2042850276","https://openalex.org/W2049500727","https://openalex.org/W2058252247","https://openalex.org/W2058898885","https://openalex.org/W2070893097","https://openalex.org/W2083797062","https://openalex.org/W2088400370","https://openalex.org/W2093855404","https://openalex.org/W2103972604","https://openalex.org/W2106221905","https://openalex.org/W2117618130","https://openalex.org/W2137130182","https://openalex.org/W2147512299","https://openalex.org/W2153919224","https://openalex.org/W2160813243","https://openalex.org/W2258054274","https://openalex.org/W2343462218","https://openalex.org/W2428204121","https://openalex.org/W2547812180","https://openalex.org/W2594059414","https://openalex.org/W2605148848","https://openalex.org/W2738537049","https://openalex.org/W2743812350","https://openalex.org/W2791255512","https://openalex.org/W2884159946","https://openalex.org/W2897753390","https://openalex.org/W2963024417","https://openalex.org/W2963472624","https://openalex.org/W3122868618","https://openalex.org/W4249610051","https://openalex.org/W4292363360"],"related_works":["https://openalex.org/W2891735857","https://openalex.org/W4214526161","https://openalex.org/W47805180","https://openalex.org/W2093953080","https://openalex.org/W2564982703","https://openalex.org/W3127610061","https://openalex.org/W2891277085","https://openalex.org/W2347172331","https://openalex.org/W3216281372","https://openalex.org/W4281643854"],"abstract_inverted_index":{"Event":[0],"detection":[1,174],"is":[2,33,91],"gaining":[3],"increasing":[4],"attention":[5],"in":[6,46,104],"smart":[7],"cities":[8],"research.":[9],"Large-scale":[10],"mobility":[11],"data":[12,54,85,178],"serves":[13],"as":[14,180,182],"an":[15,109],"important":[16],"tool":[17],"to":[18,42,51,66,79,93,112,213],"uncover":[19],"the":[20,31,101,114,120,143,154,190,194,198,220],"dynamics":[21],"of":[22,100,124,145,148,156,197],"urban":[23,95],"transportation":[24],"systems,":[25],"and":[26,50,76,82,152,172,193,217,229],"more":[27],"often":[28],"than":[29],"not":[30],"dataset":[32,211],"incomplete.":[34],"In":[35],"this":[36],"article,":[37],"we":[38,59,203],"develop":[39,108],"a":[40,61,149,208],"method":[41,123,206],"detect":[43,219],"extreme":[44],"events":[45,221,232],"large":[47,94,231],"traffic":[48,105,210,236],"datasets,":[49],"impute":[52,83],"missing":[53,84,177],"during":[55],"regular":[56],"conditions.":[57,88,161],"Specifically,":[58],"propose":[60],"robust":[62],"tensor":[63,115,135,151,191],"recovery":[64,116,171],"problem":[65,117],"recover":[67,142],"low-rank":[68,150],"tensors":[69],"under":[70,86,159,184],"fiber-sparse":[71],"corruptions":[72],"with":[73,129,176],"partial":[74],"observations,":[75],"use":[77],"it":[78],"identify":[80],"events,":[81],"typical":[87],"Our":[89],"approach":[90],"scalable":[92],"areas,":[96],"taking":[97],"full":[98],"advantage":[99],"spatio-temporal":[102],"correlations":[103],"patterns.":[106],"We":[107],"efficient":[110],"algorithm":[111,139,167],"solve":[113],"based":[118],"on":[119,189,207],"alternating":[121],"direction":[122],"multipliers":[125],"(ADMM)":[126],"framework.":[127],"Compared":[128],"existing":[130],"l":[131],"1":[132],"norm":[133],"regularized":[134],"decomposition":[136],"methods,":[137],"our":[138,166,205],"can":[140,168],"exactly":[141],"values":[144],"uncorrupted":[146],"fibers":[147,158],"find":[153],"positions":[155],"corrupted":[157],"mild":[160],"Numerical":[162],"experiments":[163],"illustrate":[164],"that":[165,233],"achieve":[169],"exact":[170],"outlier":[173],"even":[175],"rates":[179],"high":[181],"40%":[183],"5%":[185],"gross":[186],"corruption,":[187],"depending":[188],"size":[192],"Tucker":[195],"rank":[196,200],"low":[199],"tensor.":[201],"Finally,":[202],"apply":[204],"real":[209],"corresponding":[212],"downtown":[214],"Nashville,":[215],"TN":[216],"successfully":[218],"like":[222],"severe":[223],"car":[224],"crashes,":[225],"construction":[226],"lane":[227],"closures,":[228],"other":[230],"cause":[234],"significant":[235],"disruptions.":[237]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-09T13:55:54.758798","created_date":"2025-10-10T00:00:00"}
