{"id":"https://openalex.org/W2600964683","doi":"https://doi.org/10.1109/iscas.2017.8050533","title":"Subspace learning in the presence of sparse structured outliers and noise","display_name":"Subspace learning in the presence of sparse structured outliers and noise","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2600964683","doi":"https://doi.org/10.1109/iscas.2017.8050533","mag":"2600964683"},"language":"en","primary_location":{"id":"doi:10.1109/iscas.2017.8050533","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2017.8050533","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5017101039","display_name":"Shervin Minaee","orcid":"https://orcid.org/0000-0001-6689-9221"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shervin Minaee","raw_affiliation_strings":["Electrical Engineering Department, New York University"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100319039","display_name":"Yao Wang","orcid":"https://orcid.org/0000-0003-3199-3802"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Wang","raw_affiliation_strings":["Electrical Engineering Department, New York University"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, New York University","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017101039"],"corresponding_institution_ids":["https://openalex.org/I57206974"],"apc_list":null,"apc_paid":null,"fwci":0.8402,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74053137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.996399998664856,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9951000213623047,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7911762595176697},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.7680485248565674},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6888808012008667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6763840317726135},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6162285804748535},{"id":"https://openalex.org/keywords/signal-subspace","display_name":"Signal subspace","score":0.5739419460296631},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5129013061523438},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.47717395424842834},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4361186921596527},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4112658202648163},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.26836466789245605}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7911762595176697},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7680485248565674},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6888808012008667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6763840317726135},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6162285804748535},{"id":"https://openalex.org/C2777121530","wikidata":"https://www.wikidata.org/wiki/Q7512739","display_name":"Signal subspace","level":4,"score":0.5739419460296631},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5129013061523438},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.47717395424842834},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4361186921596527},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4112658202648163},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26836466789245605}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas.2017.8050533","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2017.8050533","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Circuits and Systems (ISCAS)","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":33,"referenced_works":["https://openalex.org/W46659105","https://openalex.org/W1506911060","https://openalex.org/W1513013675","https://openalex.org/W1535602073","https://openalex.org/W1859757340","https://openalex.org/W1866504676","https://openalex.org/W1925418734","https://openalex.org/W2050058873","https://openalex.org/W2073640678","https://openalex.org/W2084418968","https://openalex.org/W2096642693","https://openalex.org/W2110872054","https://openalex.org/W2128728535","https://openalex.org/W2131628350","https://openalex.org/W2134378185","https://openalex.org/W2141608131","https://openalex.org/W2164278908","https://openalex.org/W2172873798","https://openalex.org/W2253940211","https://openalex.org/W2582191739","https://openalex.org/W2598177887","https://openalex.org/W2963556179","https://openalex.org/W2963958726","https://openalex.org/W3124680869","https://openalex.org/W4238202755","https://openalex.org/W4292363360","https://openalex.org/W4295256434","https://openalex.org/W6602002561","https://openalex.org/W6630108103","https://openalex.org/W6671274415","https://openalex.org/W6674909129","https://openalex.org/W6679973066","https://openalex.org/W6732607863"],"related_works":["https://openalex.org/W1995723671","https://openalex.org/W2164647769","https://openalex.org/W2153912708","https://openalex.org/W2379264914","https://openalex.org/W2118094278","https://openalex.org/W2639400695","https://openalex.org/W2949173582","https://openalex.org/W4300048032","https://openalex.org/W2089410207","https://openalex.org/W2367577754"],"abstract_inverted_index":{"Subspace":[0],"learning":[1,57,99],"is":[2,35,122,128],"an":[3,88],"important":[4],"problem,":[5,95],"which":[6,42,96],"has":[7,108],"many":[8,30],"applications":[9],"in":[10,29,63,147],"image":[11,116,125],"and":[12,26,40,69,80,102,118,127,149,153],"video":[13],"processing.":[14],"It":[15],"can":[16],"be":[17],"used":[18,123],"to":[19,75,130],"find":[20],"a":[21,53,58,112],"low-dimensional":[22],"representation":[23],"of":[24,66,115],"signals":[25],"images.":[27,85],"But":[28],"applications,":[31],"the":[32,45,64,78,82,100,104,119],"desired":[33],"signal":[34,61],"heavily":[36],"distorted":[37],"by":[38],"outliers":[39,68,79],"noise,":[41],"negatively":[43],"affect":[44],"learned":[46,120],"subspace.":[47],"In":[48],"this":[49,94],"work,":[50],"we":[51],"present":[52,87],"novel":[54],"algorithm":[55,73,91,107],"for":[56,60,84,92,124],"subspace":[59,83,101,121],"representation,":[62],"presence":[65],"structured":[67],"noise.":[70],"The":[71],"proposed":[72],"tries":[74],"jointly":[76],"detect":[77],"learn":[81],"We":[86],"alternating":[89],"optimization":[90],"solving":[93],"iterates":[97],"between":[98],"finding":[103],"outliers.":[105],"This":[106],"been":[109],"trained":[110],"on":[111],"large":[113],"number":[114],"patches,":[117],"segmentation,":[126],"shown":[129],"achieve":[131],"better":[132],"segmentation":[133,146],"results":[134],"than":[135],"prior":[136],"methods,":[137],"including":[138],"least":[139],"absolute":[140],"deviation":[141],"fitting,":[142],"k-means":[143],"clustering":[144],"based":[145],"DjVu,":[148],"shape":[150],"primitive":[151],"extraction":[152],"coding":[154],"algorithm.":[155]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
