{"id":"https://openalex.org/W4323022369","doi":"https://doi.org/10.1109/lgrs.2023.3251861","title":"Randomized Tensor Robust PCA for Noisy Hyperspectral Image Classification","display_name":"Randomized Tensor Robust PCA for Noisy Hyperspectral Image Classification","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4323022369","doi":"https://doi.org/10.1109/lgrs.2023.3251861"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3251861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3251861","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5081502469","display_name":"Mohammad Mohammadpour Salut","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohammad M. Salut","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033437141","display_name":"David V. Anderson","orcid":"https://orcid.org/0000-0002-8571-4613"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David V. Anderson","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5081502469"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":2.9357,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.90694176,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.7612088918685913},{"id":"https://openalex.org/keywords/robust-principal-component-analysis","display_name":"Robust principal component analysis","score":0.6838843822479248},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6250839233398438},{"id":"https://openalex.org/keywords/singular-value","display_name":"Singular value","score":0.6193104982376099},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.599503219127655},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5698896050453186},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.5602801442146301},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5436917543411255},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4885138273239136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4546343684196472},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44297367334365845},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44079670310020447},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.437541127204895},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.25129464268684387},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.1275080442428589},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09843012690544128},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.09073516726493835}],"concepts":[{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.7612088918685913},{"id":"https://openalex.org/C2777749129","wikidata":"https://www.wikidata.org/wiki/Q17148469","display_name":"Robust principal component analysis","level":3,"score":0.6838843822479248},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6250839233398438},{"id":"https://openalex.org/C109282560","wikidata":"https://www.wikidata.org/wiki/Q4166054","display_name":"Singular value","level":3,"score":0.6193104982376099},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.599503219127655},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5698896050453186},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.5602801442146301},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5436917543411255},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4885138273239136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4546343684196472},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44297367334365845},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44079670310020447},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.437541127204895},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25129464268684387},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.1275080442428589},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09843012690544128},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.09073516726493835},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2023.3251861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3251861","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1992426838","https://openalex.org/W2038577255","https://openalex.org/W2043571470","https://openalex.org/W2057513844","https://openalex.org/W2117756735","https://openalex.org/W2129891925","https://openalex.org/W2145962650","https://openalex.org/W2148717137","https://openalex.org/W2157621128","https://openalex.org/W2161073299","https://openalex.org/W2171520281","https://openalex.org/W2343108676","https://openalex.org/W2741786247","https://openalex.org/W2945316973","https://openalex.org/W2962760844","https://openalex.org/W2963885538","https://openalex.org/W2979499749","https://openalex.org/W2989590988","https://openalex.org/W3093896170","https://openalex.org/W3104418202","https://openalex.org/W3155819029","https://openalex.org/W3158091052","https://openalex.org/W3171291078","https://openalex.org/W3179935684","https://openalex.org/W3216286842","https://openalex.org/W4210824752","https://openalex.org/W4224004319","https://openalex.org/W4285120820","https://openalex.org/W4290043202"],"related_works":["https://openalex.org/W4319586039","https://openalex.org/W4382583540","https://openalex.org/W1607100495","https://openalex.org/W1990844505","https://openalex.org/W2148568324","https://openalex.org/W2906988372","https://openalex.org/W2010100052","https://openalex.org/W2776058083","https://openalex.org/W2730482097","https://openalex.org/W4295239187"],"abstract_inverted_index":{"Removing":[0],"noise":[1,26],"from":[2,57],"hyperspectral":[3,28,55],"images":[4],"can":[5],"be":[6],"very":[7],"beneficial":[8],"for":[9,92,157],"improving":[10],"classification":[11,193],"accuracy.":[12,194],"Recently,":[13],"tensor":[14,36,70,99,124],"robust":[15],"principal":[16],"component":[17],"analysis":[18],"(TRPCA)":[19],"has":[20],"been":[21],"successfully":[22],"employed":[23,50],"to":[24,51,82,96,106,136,172],"reduce":[25],"in":[27,115],"images.":[29],"In":[30,148],"TRPCA,":[31],"a":[32,35,40,129,132,153],"minimization":[33,64,127],"involving":[34],"nuclear":[37,62,125],"norm":[38,63,126],"and":[39,104,160],"<inline-formula":[41],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[42],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[43],"<tex-math":[44],"notation=\"LaTeX\">$\\ell":[45],"_{1}$":[46],"</tex-math></inline-formula>":[47],"-norm":[48],"is":[49,65,94,128,135,181],"separate":[52],"the":[53,58,83,86,108,112,116,121,139,146,163,167,188],"low-rank":[54,130],"image":[56],"sparse":[59],"noise.":[60],"Tensor":[61],"solved":[66],"by":[67,166],"iteratively":[68],"performing":[69,185],"singular-value":[71],"thresholding":[72],"(T-SVT).":[73],"However,":[74,119],"TRPCA":[75,186],"possesses":[76],"high":[77],"computational":[78],"complexity":[79],"primarily":[80],"due":[81],"implementation":[84],"of":[85,111,123],"T-SVT":[87,93],"operator.":[88],"The":[89],"conventional":[90],"approach":[91],"first":[95],"perform":[97],"full":[98,189],"singular":[100,109,140],"value":[101],"decomposition":[102],"(T-SVD)":[103],"then":[105],"shrink":[107],"values":[110,141],"frontal":[113],"slices":[114],"frequency":[117],"domain.":[118],"when":[120],"solution":[122],"tensor,":[131],"good":[133],"strategy":[134],"incrementally":[137],"find":[138],"until":[142],"they":[143],"fall":[144],"below":[145],"threshold.":[147],"this":[149],"letter,":[150],"we":[151,161],"propose":[152],"randomized":[154],"blocked":[155],"algorithm":[156],"computing":[158],"T-SVT,":[159],"leverage":[162],"compression":[164],"attained":[165],"fast":[168],"Fourier":[169],"transform":[170],"(FFT)":[171],"accelerate":[173],"TRPCA.":[174],"Numerical":[175],"experiments":[176],"indicate":[177],"that":[178],"our":[179],"method":[180],"significantly":[182],"faster":[183],"than":[184],"via":[187],"T-SVD":[190],"while":[191],"maintaining":[192]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2026-04-12T07:58:50.170612","created_date":"2025-10-10T00:00:00"}