{"id":"https://openalex.org/W1534242820","doi":"https://doi.org/10.1109/icassp.2015.7178334","title":"Removing data with noisy responses in regression analysis","display_name":"Removing data with noisy responses in regression analysis","publication_year":2015,"publication_date":"2015-04-01","ids":{"openalex":"https://openalex.org/W1534242820","doi":"https://doi.org/10.1109/icassp.2015.7178334","mag":"1534242820"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2015.7178334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2015.7178334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5072160392","display_name":"Alan Wisler","orcid":"https://orcid.org/0000-0003-2601-2846"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alan Wisler","raw_affiliation_strings":["School of ECEE and Dept. of SHS, ASU"],"affiliations":[{"raw_affiliation_string":"School of ECEE and Dept. of SHS, ASU","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021646973","display_name":"Visar Berisha","orcid":"https://orcid.org/0000-0001-8804-8874"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Visar Berisha","raw_affiliation_strings":["School of ECEE and Dept. of SHS, ASU"],"affiliations":[{"raw_affiliation_string":"School of ECEE and Dept. of SHS, ASU","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081874896","display_name":"Karthikeyan Natesan Ramamurthy","orcid":"https://orcid.org/0000-0002-6021-5930"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karthikeyan Ramamurthy","raw_affiliation_strings":["IBM Thomas J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074371899","display_name":"Andreas Spanias","orcid":"https://orcid.org/0000-0003-0306-9348"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andreas Spanias","raw_affiliation_strings":["ASU SenSIP Genter","School of ECEE and Dept. of SHS, ASU"],"affiliations":[{"raw_affiliation_string":"ASU SenSIP Genter","institution_ids":[]},{"raw_affiliation_string":"School of ECEE and Dept. of SHS, ASU","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056934840","display_name":"Julie Liss","orcid":"https://orcid.org/0000-0001-8782-2901"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Julie Liss","raw_affiliation_strings":["School of ECEE and Dept. of SHS, ASU"],"affiliations":[{"raw_affiliation_string":"School of ECEE and Dept. of SHS, ASU","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5072160392"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8629,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81294209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"134","issue":null,"first_page":"2066","last_page":"2070"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9977999925613403,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9977999925613403,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9969000220298767,"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":0.996399998664856,"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":0.7764438390731812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6811752319335938},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5917197465896606},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5663005709648132},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.5324175357818604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5123401880264282},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44678768515586853},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.43746837973594666},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4364458918571472},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.410602331161499},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4068470299243927},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3671550750732422},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36226940155029297},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2937166094779968},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2378775179386139}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7764438390731812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6811752319335938},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5917197465896606},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5663005709648132},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.5324175357818604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5123401880264282},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44678768515586853},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.43746837973594666},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4364458918571472},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.410602331161499},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4068470299243927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3671550750732422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36226940155029297},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2937166094779968},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2378775179386139},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2015.7178334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2015.7178334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W655565512","https://openalex.org/W1964357740","https://openalex.org/W1964877831","https://openalex.org/W1965864703","https://openalex.org/W1977556410","https://openalex.org/W1989223358","https://openalex.org/W2010135967","https://openalex.org/W2023126832","https://openalex.org/W2027390033","https://openalex.org/W2027913322","https://openalex.org/W2029565994","https://openalex.org/W2033207010","https://openalex.org/W2050551672","https://openalex.org/W2056500762","https://openalex.org/W2062235204","https://openalex.org/W2085703180","https://openalex.org/W2090236122","https://openalex.org/W2122111042","https://openalex.org/W2125943921","https://openalex.org/W2129249398","https://openalex.org/W2134305421","https://openalex.org/W2150593711","https://openalex.org/W2498631646","https://openalex.org/W3100570787","https://openalex.org/W4210694145","https://openalex.org/W4238202755","https://openalex.org/W6679959949","https://openalex.org/W6785690361"],"related_works":["https://openalex.org/W71955863","https://openalex.org/W2359185137","https://openalex.org/W2610918223","https://openalex.org/W2085200861","https://openalex.org/W2567300168","https://openalex.org/W2184115898","https://openalex.org/W2789383625","https://openalex.org/W1550998911","https://openalex.org/W2786391746","https://openalex.org/W3132346564"],"abstract_inverted_index":{"In":[0,19],"regression":[1,30],"analysis,":[2],"outliers":[3],"in":[4,11,16],"the":[5,12,29,41,60,65,68,92],"data":[6,55],"can":[7],"induce":[8],"a":[9,34],"bias":[10],"learned":[13],"function,":[14],"resulting":[15],"larger":[17],"errors.":[18],"this":[20,44],"paper":[21],"we":[22,48],"derive":[23],"an":[24,50],"empirically":[25],"estimable":[26],"bound":[27,45],"on":[28,33,70,99],"error":[31],"based":[32],"Euclidean":[35],"minimum":[36],"spanning":[37],"tree":[38],"generated":[39],"from":[40,59,77],"data.":[42,102],"Using":[43],"as":[46],"motivation,":[47],"propose":[49],"iterative":[51],"approach":[52,94],"to":[53],"remove":[54],"with":[56,72,79],"noisy":[57,87],"responses":[58],"training":[61,90],"set.":[62],"We":[63],"evaluate":[64],"performance":[66,98],"of":[67],"algorithm":[69],"experiments":[71],"real-world":[73],"pathological":[74],"speech":[75],"(speech":[76],"individuals":[78],"neurogenic":[80],"disorders).":[81],"Comparative":[82],"results":[83],"show":[84],"that":[85],"removing":[86],"examples":[88],"during":[89],"using":[91],"proposed":[93],"yields":[95],"better":[96],"predictive":[97],"out-of-":[100],"sample":[101]},"counts_by_year":[{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
