{"id":"https://openalex.org/W2791785025","doi":"https://doi.org/10.1109/camsap.2017.8313071","title":"Robust Low-Complexity methods for matrix column outlier identification","display_name":"Robust Low-Complexity methods for matrix column outlier identification","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2791785025","doi":"https://doi.org/10.1109/camsap.2017.8313071","mag":"2791785025"},"language":"en","primary_location":{"id":"doi:10.1109/camsap.2017.8313071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2017.8313071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101550432","display_name":"Xingguo Li","orcid":"https://orcid.org/0000-0002-5510-9447"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xingguo Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota Twin Cities, Minneapolis, MN, 55455"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota Twin Cities, Minneapolis, MN, 55455","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011305900","display_name":"Jarvis Haupt","orcid":"https://orcid.org/0000-0002-1570-7071"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jarvis Haupt","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota Twin Cities, Minneapolis, MN, 55455"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota Twin Cities, Minneapolis, MN, 55455","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33842265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"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":0.9997000098228455,"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":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/outlier","display_name":"Outlier","score":0.8484433889389038},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6802666187286377},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"Sample complexity","score":0.623778223991394},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5983463525772095},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.578990638256073},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5778287649154663},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5704336762428284},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5611518621444702},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5547760128974915},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5524995923042297},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.52657550573349},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5109671354293823},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4427127242088318},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4358682334423065},{"id":"https://openalex.org/keywords/column","display_name":"Column (typography)","score":0.4143863916397095},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3549283444881439},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33814308047294617},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21398064494132996},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09618726372718811}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8484433889389038},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6802666187286377},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.623778223991394},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5983463525772095},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.578990638256073},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5778287649154663},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5704336762428284},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5611518621444702},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5547760128974915},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5524995923042297},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.52657550573349},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5109671354293823},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4427127242088318},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4358682334423065},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.4143863916397095},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3549283444881439},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33814308047294617},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21398064494132996},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09618726372718811},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/camsap.2017.8313071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2017.8313071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W272277767","https://openalex.org/W1613169971","https://openalex.org/W1670485642","https://openalex.org/W1970833585","https://openalex.org/W1995168330","https://openalex.org/W2003753589","https://openalex.org/W2018341990","https://openalex.org/W2087445541","https://openalex.org/W2096608935","https://openalex.org/W2139054653","https://openalex.org/W2142077116","https://openalex.org/W2142651607","https://openalex.org/W2145962650","https://openalex.org/W2157578436","https://openalex.org/W2221507943","https://openalex.org/W2290632462","https://openalex.org/W2510601019","https://openalex.org/W2560103694","https://openalex.org/W2594059414","https://openalex.org/W2611906854","https://openalex.org/W2798440305","https://openalex.org/W2962933442","https://openalex.org/W2963363076","https://openalex.org/W2979473749","https://openalex.org/W3102566946","https://openalex.org/W3103526270","https://openalex.org/W3104624268","https://openalex.org/W3124680869","https://openalex.org/W6636395559","https://openalex.org/W6637049281","https://openalex.org/W6681016373","https://openalex.org/W6682218280","https://openalex.org/W6750389826","https://openalex.org/W6789230361"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2357409937","https://openalex.org/W2978674666","https://openalex.org/W2074430941","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W3107369729"],"abstract_inverted_index":{"This":[0],"paper":[1],"examines":[2],"the":[3,18,24,43,58,71,76,80],"problem":[4],"of":[5,79],"locating":[6,57],"outlier":[7],"columns":[8],"in":[9,15,55],"a":[10],"large,":[11],"otherwise":[12],"low-rank":[13],"matrix,":[14],"settings":[16],"where":[17,23],"data":[19],"are":[20,67],"noisy,":[21],"or":[22],"overall":[25],"matrix":[26],"has":[27],"missing":[28],"elements.":[29],"We":[30],"propose":[31],"an":[32],"efficient":[33],"randomized":[34],"two-step":[35],"inference":[36],"framework,":[37],"and":[38,74],"establish":[39],"sufficient":[40],"conditions":[41],"on":[42],"required":[44],"sample":[45],"complexities":[46],"under":[47],"which":[48],"these":[49],"methods":[50],"succeed":[51],"(with":[52],"high":[53],"probability)":[54],"accurately":[56],"outliers":[59],"for":[60],"each":[61],"task.":[62],"Comprehensive":[63],"numerical":[64],"experimental":[65],"results":[66],"provided":[68],"to":[69],"validate":[70],"theoretical":[72],"bounds":[73],"demonstrate":[75],"computational":[77],"efficiency":[78],"proposed":[81],"algorithm.":[82]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
