{"id":"https://openalex.org/W2593727658","doi":"https://doi.org/10.1109/globalsip.2016.7905826","title":"Active regression with compressive-sensing based outlier mitigation for both small and large outliers","display_name":"Active regression with compressive-sensing based outlier mitigation for both small and large outliers","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2593727658","doi":"https://doi.org/10.1109/globalsip.2016.7905826","mag":"2593727658"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2016.7905826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2016.7905826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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/A5089650246","display_name":"Jian Zheng","orcid":"https://orcid.org/0000-0001-9741-9526"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jian Zheng","raw_affiliation_strings":["Department of ECE, State University of New York at Binghamton, Binghamton, NY"],"affiliations":[{"raw_affiliation_string":"Department of ECE, State University of New York at Binghamton, Binghamton, NY","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100434102","display_name":"Xiaohua Li","orcid":"https://orcid.org/0000-0002-1209-7837"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohua Li","raw_affiliation_strings":["Department of ECE, State University of New York at Binghamton, Binghamton, NY"],"affiliations":[{"raw_affiliation_string":"Department of ECE, State University of New York at Binghamton, Binghamton, NY","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5089650246"],"corresponding_institution_ids":["https://openalex.org/I123946342"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11657467,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"172","last_page":"176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998999834060669,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9980999827384949,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9304161667823792},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7454751133918762},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700446367263794},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6277439594268799},{"id":"https://openalex.org/keywords/robust-regression","display_name":"Robust regression","score":0.5843191146850586},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5463292598724365},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5377367734909058},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.522031307220459},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.49615463614463806},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4938543438911438},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.47193023562431335},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47078245878219604},{"id":"https://openalex.org/keywords/robust-statistics","display_name":"Robust statistics","score":0.4534105062484741},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4121643304824829},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41118037700653076},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14435014128684998},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14255961775779724}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.9304161667823792},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7454751133918762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700446367263794},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6277439594268799},{"id":"https://openalex.org/C70259352","wikidata":"https://www.wikidata.org/wiki/Q1847839","display_name":"Robust regression","level":3,"score":0.5843191146850586},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5463292598724365},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5377367734909058},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.522031307220459},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.49615463614463806},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4938543438911438},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.47193023562431335},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47078245878219604},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.4534105062484741},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4121643304824829},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41118037700653076},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14435014128684998},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14255961775779724},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip.2016.7905826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2016.7905826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1993615002","https://openalex.org/W2014896416","https://openalex.org/W2026386069","https://openalex.org/W2040731319","https://openalex.org/W2050058873","https://openalex.org/W2085261163","https://openalex.org/W2101223300","https://openalex.org/W2119046642","https://openalex.org/W2129249398","https://openalex.org/W2152139090","https://openalex.org/W2156324002","https://openalex.org/W2160828669","https://openalex.org/W2167157155","https://openalex.org/W2264960680","https://openalex.org/W2343970291","https://openalex.org/W2396874021","https://openalex.org/W2498631646","https://openalex.org/W2570764145","https://openalex.org/W2949071206","https://openalex.org/W2952875011","https://openalex.org/W3124680869","https://openalex.org/W4298132949","https://openalex.org/W6683185833","https://openalex.org/W6693126768","https://openalex.org/W6704441444","https://openalex.org/W7073710283"],"related_works":["https://openalex.org/W3121219282","https://openalex.org/W1585351680","https://openalex.org/W2999599821","https://openalex.org/W2046343964","https://openalex.org/W3121734683","https://openalex.org/W4233227754","https://openalex.org/W4256210798","https://openalex.org/W4287749375","https://openalex.org/W3038621715","https://openalex.org/W1600426151"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3,94],"new":[4,95],"active":[5,33],"learning":[6],"scheme":[7,125],"is":[8,36,54,97],"proposed":[9,124],"for":[10],"linear":[11],"regression":[12,34],"problems":[13],"with":[14],"the":[15,19,25,40,47,61,80,120,123],"objective":[16],"of":[17,122],"resolving":[18],"insufficient":[20],"training":[21,27,42,127],"data":[22,28,43,49,112,128],"problem":[23],"and":[24,65,110,130],"unreliable":[26],"labeling":[29,58,82],"problem.":[30],"A":[31],"pool-based":[32],"technique":[35,96],"applied":[37],"to":[38,44,56,78,99,118],"select":[39],"optimal":[41],"label":[45],"from":[46],"overall":[48],"pool.":[50],"Then,":[51],"compressive":[52],"sensing":[53],"exploited":[55],"remove":[57],"errors":[59,62,83],"if":[60],"are":[63,71,90,116],"sparse":[64,104],"have":[66,85],"large":[67,73,105],"enough":[68],"magnitudes,":[69,88],"which":[70,89],"called":[72,91],"outliers.":[74,106],"Next,":[75],"in":[76,126],"order":[77],"mitigate":[79],"non-sparse":[81],"that":[84],"relatively":[86],"small":[87,92],"outliers,":[93],"developed":[98],"convert":[100],"them":[101],"back":[102],"into":[103],"With":[107],"both":[108],"artificial":[109],"real":[111],"sets,":[113],"extensive":[114],"simulations":[115],"conducted":[117],"verify":[119],"robustness":[121],"selection":[129],"outlier":[131],"suppression.":[132]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
