{"id":"https://openalex.org/W7131422189","doi":"https://doi.org/10.1109/icdm65498.2025.00050","title":"Spline-Interpolated Density and Dependency-Aware Outlier Scoring for Lightweight Deployment","display_name":"Spline-Interpolated Density and Dependency-Aware Outlier Scoring for Lightweight Deployment","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7131422189","doi":"https://doi.org/10.1109/icdm65498.2025.00050"},"language":null,"primary_location":{"id":"doi:10.1109/icdm65498.2025.00050","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining (ICDM)","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/A5013466201","display_name":"A. Li","orcid":"https://orcid.org/0000-0001-6882-7969"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Angela Li","raw_affiliation_strings":["Applied Mathematics and Physics, Stony Brook University,Stony Brook,NY"],"affiliations":[{"raw_affiliation_string":"Applied Mathematics and Physics, Stony Brook University,Stony Brook,NY","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126825149","display_name":"David Li","orcid":null},"institutions":[{"id":"https://openalex.org/I19772626","display_name":"Yeshiva University","ror":"https://ror.org/045x93337","country_code":"US","type":"education","lineage":["https://openalex.org/I19772626"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Li","raw_affiliation_strings":["Katz School of Science and Health, Yeshiva University,New York,NY"],"affiliations":[{"raw_affiliation_string":"Katz School of Science and Health, Yeshiva University,New York,NY","institution_ids":["https://openalex.org/I19772626"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5013466201"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71578328,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"427","last_page":"436"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.382999986410141,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.382999986410141,"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/T10904","display_name":"Embedded Systems Design Techniques","score":0.09589999914169312,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.05979999899864197,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.8169000148773193},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7964000105857849},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6194999814033508},{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.5932999849319458},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5493000149726868},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4683000147342682},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.46639999747276306},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.46059998869895935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4259999990463257}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8169000148773193},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7964000105857849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6486999988555908},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6194999814033508},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6171000003814697},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.5932999849319458},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5493000149726868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4991999864578247},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4683000147342682},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.46639999747276306},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.40560001134872437},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3158000111579895},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.2890999913215637},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.260699987411499},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm65498.2025.00050","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.44377440214157104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2069051541","https://openalex.org/W2122646361","https://openalex.org/W2132870739","https://openalex.org/W2134490011","https://openalex.org/W2964145760","https://openalex.org/W3022118908","https://openalex.org/W3023062847","https://openalex.org/W3128465814","https://openalex.org/W4226060502","https://openalex.org/W4254182148","https://openalex.org/W4380987383","https://openalex.org/W4400617507"],"related_works":[],"abstract_inverted_index":{"Detecting":[0],"outliers":[1,171],"in":[2,12,172],"high-dimensional":[3],"data":[4],"under":[5],"resource":[6],"constraints":[7],"remains":[8],"a":[9,47,59,90,117,132,178],"central":[10],"challenge":[11],"real-world":[13],"deployments":[14],"such":[15],"as":[16],"wearables,":[17],"IoT,":[18],"and":[19,75,98,109,126,155,161,169,182,192],"edge":[20],"systems.":[21],"Existing":[22],"lightweight":[23],"methods":[24],"often":[25],"assume":[26],"feature":[27],"independence,":[28],"which":[29],"limits":[30],"their":[31],"ability":[32],"to":[33],"capture":[34],"joint":[35,61,114,170],"anomalies":[36],"that":[37,52,121,137,150,187],"arise":[38],"from":[39],"multivariate":[40,127],"dependencies.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45],"introduce":[46],"semi-parametric":[48],"outlier":[49,119,184],"detection":[50,166,185],"framework":[51],"extends":[53],"spline-interpolated":[54],"marginal":[55,124],"density":[56,115,190],"estimation":[57],"with":[58,159],"copula-based":[60],"dependency":[62],"model.":[63],"Our":[64],"approach":[65],"estimates":[66],"smooth,":[67],"non-parametric":[68],"univariate":[69],"densities":[70],"using":[71,89],"compact":[72],"cubic":[73],"splines":[74],"transforms":[76],"inputs":[77],"into":[78],"uniform":[79],"space":[80],"via":[81],"empirical":[82],"CDFs.":[83],"We":[84,129],"then":[85],"model":[86],"inter-feature":[87],"dependencies":[88],"flexible":[91],"family":[92],"of":[93,167],"copulas\u2014including":[94],"elliptical":[95],"(Gaussian,":[96],"t)":[97],"Archimedean":[99],"types\u2014and":[100],"automatically":[101],"select":[102],"the":[103],"optimal":[104],"copula":[105],"based":[106],"on":[107],"AIC":[108],"BIC":[110],"scores.":[111],"The":[112],"resulting":[113],"yields":[116],"probabilistic":[118],"score":[120],"captures":[122],"both":[123],"extremity":[125],"atypicality.":[128],"further":[130],"derive":[131],"Bayesian":[133],"posterior":[134],"scoring":[135],"variant":[136],"incorporates":[138],"prior":[139],"assumptions":[140],"about":[141],"anomaly":[142,194],"prevalence":[143],"without":[144],"requiring":[145],"supervision.":[146],"Extensive":[147],"analysis":[148],"demonstrates":[149],"our":[151],"method":[152],"maintains":[153],"computational":[154],"memory":[156],"efficiency":[157],"competitive":[158],"HBOS":[160],"ECOD,":[162],"while":[163],"significantly":[164],"improving":[165],"contextual":[168],"real-time":[173],"settings.":[174],"This":[175],"work":[176],"contributes":[177],"statistically":[179],"grounded,":[180],"scalable,":[181],"deployable":[183],"paradigm":[186],"unifies":[188],"interpretable":[189],"modeling":[191],"dependency-aware":[193],"reasoning.":[195]},"counts_by_year":[],"updated_date":"2026-02-27T06:17:20.405678","created_date":"2026-02-26T00:00:00"}