{"id":"https://openalex.org/W2800394871","doi":"https://doi.org/10.1109/icassp.2018.8461693","title":"Outlier Removal for Enhancing Kernel-Based Classifier Via the Discriminant Information","display_name":"Outlier Removal for Enhancing Kernel-Based Classifier Via the Discriminant Information","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2800394871","doi":"https://doi.org/10.1109/icassp.2018.8461693","mag":"2800394871"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2018.8461693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8461693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 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/A5007869327","display_name":"Thee Chanyaswad","orcid":null},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Thee Chanyaswad","raw_affiliation_strings":["Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066716180","display_name":"Mert Al","orcid":"https://orcid.org/0000-0001-6773-0612"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mert Al","raw_affiliation_strings":["Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072714962","display_name":"Sun\u2010Yuan Kung","orcid":"https://orcid.org/0000-0002-7314-0720"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S. Y. Kung","raw_affiliation_strings":["Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007869327"],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.03994251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"6","issue":null,"first_page":"2266","last_page":"2270"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T12535","display_name":"Machine Learning and Data Classification","score":0.9973000288009644,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9894999861717224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7177680730819702},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6709483861923218},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6682760715484619},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6401443481445312},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6026386022567749},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.48765671253204346},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4813400208950043},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4496666193008423},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.41850265860557556},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41636621952056885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4141993820667267},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23387956619262695}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7177680730819702},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6709483861923218},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682760715484619},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6401443481445312},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6026386022567749},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.48765671253204346},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4813400208950043},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4496666193008423},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.41850265860557556},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41636621952056885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4141993820667267},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23387956619262695},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2018.8461693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8461693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7099999785423279,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W134960717","https://openalex.org/W576428146","https://openalex.org/W1480376833","https://openalex.org/W1971633085","https://openalex.org/W1985995090","https://openalex.org/W1994935303","https://openalex.org/W2001619934","https://openalex.org/W2035221948","https://openalex.org/W2040884411","https://openalex.org/W2054080856","https://openalex.org/W2056532343","https://openalex.org/W2103164654","https://openalex.org/W2104867159","https://openalex.org/W2118563516","https://openalex.org/W2123395972","https://openalex.org/W2124351082","https://openalex.org/W2135106139","https://openalex.org/W2140801466","https://openalex.org/W2143426320","https://openalex.org/W2144902422","https://openalex.org/W2277202671","https://openalex.org/W2552458324","https://openalex.org/W2577492210","https://openalex.org/W2735852363","https://openalex.org/W2735925300","https://openalex.org/W2771885995","https://openalex.org/W3120740533","https://openalex.org/W3144619878","https://openalex.org/W4229706427","https://openalex.org/W4239510810","https://openalex.org/W4239944110","https://openalex.org/W6605479355","https://openalex.org/W6649117672","https://openalex.org/W6675791501","https://openalex.org/W6677614981","https://openalex.org/W6678945949","https://openalex.org/W6680272842","https://openalex.org/W6680429928","https://openalex.org/W6681302627","https://openalex.org/W6694736540","https://openalex.org/W6729810091","https://openalex.org/W6741189626","https://openalex.org/W7043766663"],"related_works":["https://openalex.org/W2499612753","https://openalex.org/W2350751952","https://openalex.org/W1999647744","https://openalex.org/W2362114017","https://openalex.org/W3111802945","https://openalex.org/W3147024994","https://openalex.org/W2063246903","https://openalex.org/W2374055396","https://openalex.org/W1978302214","https://openalex.org/W2021817983"],"abstract_inverted_index":{"Pattern":[0],"recognition":[1],"on":[2,145,180,189],"big":[3,209],"data":[4,32,41,84,210],"can":[5,117,163,195],"be":[6,118],"challenging":[7],"for":[8,208],"kernel":[9,46,206],"machines":[10,47,207],"as":[11],"the":[12,16,30,45,50,57,61,68,71,79,83,92,101,107,111,114,122,134,139,150,167,170,181,190,205],"complexity":[13,58],"grows":[14],"with":[15,49,121,157],"squared":[17],"number":[18,62],"of":[19,63,82,103,110,154,169,204],"training":[20,64],"samples.":[21,65],"In":[22],"this":[23,27,201],"work,":[24],"we":[25,162],"overcome":[26],"hurdle":[28],"via":[29,91,160],"outlying":[31],"sample":[33,105,151],"removal":[34,73,152,159],"pre-processing":[35],"step.":[36],"This":[37,88],"approach":[38],"removes":[39],"less-informative":[40],"samples":[42],"and":[43,53,137,175,187],"trains":[44],"only":[48],"remaining":[51],"data,":[52],"hence,":[54],"directly":[55],"reduces":[56],"by":[59,172,185],"reducing":[60],"To":[66],"enhance":[67,138],"classification":[69,140],"performance,":[70],"outlier":[72,158],"process":[74],"is":[75,85,89],"done":[76],"such":[77],"that":[78],"discriminant":[80,108],"information":[81,109],"mostly":[86],"intact.":[87],"achieved":[90],"novel":[93],"Outlier-Removal":[94],"Discriminant":[95],"Information":[96],"(ORDI)":[97],"metric,":[98],"which":[99],"measures":[100],"contribution":[102],"each":[104],"toward":[106],"dataset.":[112],"Hence,":[113,193],"ORDI":[115,194],"metric":[116],"used":[119],"together":[120],"simple":[123],"filter":[124],"method":[125],"to":[126,131],"effectively":[127],"remove":[128],"insignificant":[129],"outliers":[130],"both":[132],"reduce":[133],"computational":[135],"cost":[136],"performance.":[141],"We":[142],"experimentally":[143],"show":[144],"two":[146,191],"real-world":[147],"datasets":[148],"at":[149],"ratio":[153],"0.2":[155],"that,":[156],"ORDI,":[161],"simultaneously":[164],"(1)":[165],"improve":[166],"accuracy":[168],"classifier":[171],"1":[173],"%,":[174],"(2)":[176],"provide":[177,196],"significant":[178],"saving":[179],"total":[182],"running":[183],"time":[184],"1.5x":[186],"2x":[188],"datasets.":[192],"a":[197],"win-win":[198],"situation":[199],"in":[200],"performance-complexity":[202],"tradeoff":[203],"analysis.":[211]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}