{"id":"https://openalex.org/W7127575362","doi":"https://doi.org/10.48550/arxiv.2602.03138","title":"SATORIS-N: Spectral Analysis based Traffic Observation Recovery via Informed Subspaces and Nuclear-norm minimization","display_name":"SATORIS-N: Spectral Analysis based Traffic Observation Recovery via Informed Subspaces and Nuclear-norm minimization","publication_year":2026,"publication_date":"2026-02-03","ids":{"openalex":"https://openalex.org/W7127575362","doi":"https://doi.org/10.48550/arxiv.2602.03138"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.03138","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125045161","display_name":"Sampad Mohanty","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohanty, Sampad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063784062","display_name":"Bhaskar Krishnamachari","orcid":"https://orcid.org/0000-0002-9994-9931"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishnamachari, Bhaskar","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5125045161"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.6412000060081482,"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":0.6412000060081482,"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/T10524","display_name":"Traffic control and management","score":0.13019999861717224,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T13282","display_name":"Automated Road and Building Extraction","score":0.0210999995470047,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/linear-subspace","display_name":"Linear subspace","score":0.5964999794960022},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4936999976634979},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.48989999294281006},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.4708000123500824},{"id":"https://openalex.org/keywords/semidefinite-programming","display_name":"Semidefinite programming","score":0.4636000096797943},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4575999975204468},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.447299987077713},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.41130000352859497},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.3952000141143799},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.37700000405311584}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6057000160217285},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5964999794960022},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.4708000123500824},{"id":"https://openalex.org/C101901036","wikidata":"https://www.wikidata.org/wiki/Q2269096","display_name":"Semidefinite programming","level":2,"score":0.4636000096797943},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.426800012588501},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4115000069141388},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.41130000352859497},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.37700000405311584},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C36299963","wikidata":"https://www.wikidata.org/wiki/Q1369844","display_name":"Observability","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.3625999987125397},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3619999885559082},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3411000072956085},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.32109999656677246},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C109282560","wikidata":"https://www.wikidata.org/wiki/Q4166054","display_name":"Singular value","level":3,"score":0.2985999882221222},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29809999465942383},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.2955000102519989},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2939999997615814},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C158968445","wikidata":"https://www.wikidata.org/wiki/Q7631150","display_name":"Subgradient method","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C136643341","wikidata":"https://www.wikidata.org/wiki/Q1361526","display_name":"Reachability","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2808000147342682},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.27950000762939453},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.03138","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.03138","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.03138","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.03138","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.6409725546836853,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Traffic-density":[0],"matrices":[1,90],"from":[2,32,91],"different":[3],"days":[4,93],"exhibit":[5],"both":[6],"low":[7,61],"rank":[8,62],"and":[9,63,71,77,136,149,179,194,224],"stable":[10],"correlations":[11],"in":[12,88,165],"their":[13],"singular-vector":[14],"subspaces.":[15],"Leveraging":[16],"this,":[17],"we":[18],"introduce":[19],"SATORIS-N,":[20],"a":[21,38,67,83],"framework":[22,159],"for":[23,222],"imputing":[24],"partially":[25],"observed":[26],"traffic-density":[27,184],"by":[28],"informed":[29],"subspace":[30,64],"priors":[31],"neighboring":[33],"days.":[34],"Our":[35],"contribution":[36],"is":[37,120],"subspace-aware":[39],"semidefinite":[40],"programming":[41],"(SDP)}":[42],"formulation":[43,58],"of":[44,99,127,176],"nuclear":[45],"norm":[46],"that":[47],"explicitly":[48],"informs":[49],"the":[50,116,174],"reconstruction":[51,185],"with":[52],"prior":[53],"singular-subspace":[54],"information.":[55],"This":[56],"convex":[57],"jointly":[59],"enforces":[60],"alignment,":[65],"providing":[66],"single":[68],"global":[69],"optimum":[70],"substantially":[72],"improving":[73],"accuracy":[74],"under":[75],"medium":[76],"high":[78,155],"occlusion.":[79],"We":[80],"also":[81],"study":[82],"lightweight":[84],"implicit":[85],"subspace-alignment}":[86],"strategy":[87],"which":[89,166],"consecutive":[92],"are":[94,114,129,205],"concatenated":[95],"to":[96,161,209],"encourage":[97],"alignment":[98],"spatial":[100],"or":[101,202,214],"temporal":[102],"singular":[103,167],"directions.":[104],"Although":[105],"this":[106],"heuristic":[107],"offers":[108],"modest":[109],"gains":[110],"when":[111,124],"missing":[112],"rates":[113],"low,":[115],"explicit":[117],"SDP":[118],"approach":[119],"markedly":[121],"more":[122],"robust":[123],"large":[125],"fractions":[126],"entries":[128],"missing.":[130],"Across":[131],"two":[132],"real-world":[133],"datasets":[134],"(Beijing":[135],"Shanghai),":[137],"SATORIS-N":[138],"consistently":[139],"outperforms":[140],"standard":[141],"matrix-completion":[142],"methods":[143],"such":[144],"as":[145],"SoftImpute,":[146],"IterativeSVD,":[147],"statistical,":[148],"even":[150],"deep":[151],"learning":[152],"baselines":[153],"at":[154],"occlusion":[156],"levels.":[157],"The":[158],"generalizes":[160],"other":[162],"spatiotemporal":[163],"settings":[164],"subspaces":[168],"evolve":[169],"slowly":[170],"over":[171],"time.":[172],"In":[173],"context":[175],"intelligent":[177],"vehicles":[178],"vehicle-to-everything":[180],"(V2X)":[181],"systems,":[182],"accurate":[183],"enables":[186],"critical":[187],"applications":[188],"including":[189],"cooperative":[190],"perception,":[191],"predictive":[192],"routing,":[193],"vehicle-to-infrastructure":[195],"(V2I)":[196],"communication":[197,210],"optimization.":[198],"When":[199],"infrastructure":[200],"sensors":[201],"vehicle-reported":[203],"observations":[204],"incomplete":[206],"-":[207],"due":[208],"dropouts,":[211],"sensor":[212],"occlusions,":[213],"sparse":[215],"connected":[216],"vehicle":[217],"penetration-reliable":[218],"imputation":[219],"becomes":[220],"essential":[221],"safe":[223],"efficient":[225],"autonomous":[226],"navigation.":[227]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-06T00:00:00"}