{"id":"https://openalex.org/W3137438615","doi":"https://doi.org/10.1137/21m1407707","title":"Interpretable Approximation of High-Dimensional Data","display_name":"Interpretable Approximation of High-Dimensional Data","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3137438615","doi":"https://doi.org/10.1137/21m1407707","mag":"3137438615"},"language":"en","primary_location":{"id":"doi:10.1137/21m1407707","is_oa":true,"landing_page_url":"https://doi.org/10.1137/21m1407707","pdf_url":null,"source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Mathematics of Data Science","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1137/21m1407707","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020926962","display_name":"Daniel Potts","orcid":"https://orcid.org/0000-0003-3651-4364"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Daniel Potts","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3651-4364","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015364825","display_name":"Michael Schmischke","orcid":"https://orcid.org/0000-0003-1152-0864"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Schmischke","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1152-0864","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020926962"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.61851425,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":"4","first_page":"1301","last_page":"1323"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9976999759674072,"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/T10320","display_name":"Neural Networks and Applications","score":0.9976999759674072,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","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/T10136","display_name":"Statistical Methods and Inference","score":0.9933000206947327,"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/interpretability","display_name":"Interpretability","score":0.9381356239318848},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.7648143768310547},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7082024812698364},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6740984916687012},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6003977060317993},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5805680155754089},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5111163258552551},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5012087821960449},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.49094322323799133},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4653528332710266},{"id":"https://openalex.org/keywords/high-dimensional","display_name":"High dimensional","score":0.4152677059173584},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.411062628030777},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3844044804573059},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38007786870002747},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3402498960494995}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9381356239318848},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.7648143768310547},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7082024812698364},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6740984916687012},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6003977060317993},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5805680155754089},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5111163258552551},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5012087821960449},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.49094322323799133},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4653528332710266},{"id":"https://openalex.org/C3019722297","wikidata":"https://www.wikidata.org/wiki/Q4440864","display_name":"High dimensional","level":2,"score":0.4152677059173584},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.411062628030777},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3844044804573059},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38007786870002747},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3402498960494995},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"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/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1137/21m1407707","is_oa":true,"landing_page_url":"https://doi.org/10.1137/21m1407707","pdf_url":null,"source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Mathematics of Data Science","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2103.13787","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.13787","pdf_url":"https://arxiv.org/pdf/2103.13787","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3137438615","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2103.13787.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2103.13787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2103.13787","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.1137/21m1407707","is_oa":true,"landing_page_url":"https://doi.org/10.1137/21m1407707","pdf_url":null,"source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Mathematics of Data Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1074951048","display_name":null,"funder_award_id":"416228727 - SFB 1410","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G3226343981","display_name":null,"funder_award_id":"01|S20053A","funder_id":"https://openalex.org/F4320321114","funder_display_name":"Bundesministerium f\u00fcr Bildung und Forschung"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W39759813","https://openalex.org/W79383899","https://openalex.org/W100464641","https://openalex.org/W572368116","https://openalex.org/W637625958","https://openalex.org/W1487825358","https://openalex.org/W1644425553","https://openalex.org/W1663973292","https://openalex.org/W1998160729","https://openalex.org/W2006891733","https://openalex.org/W2051344283","https://openalex.org/W2053469438","https://openalex.org/W2055663168","https://openalex.org/W2076723282","https://openalex.org/W2082290707","https://openalex.org/W2092939357","https://openalex.org/W2097897435","https://openalex.org/W2100179204","https://openalex.org/W2101589741","https://openalex.org/W2107411554","https://openalex.org/W2107791152","https://openalex.org/W2131768055","https://openalex.org/W2136128169","https://openalex.org/W2144902422","https://openalex.org/W2156566935","https://openalex.org/W2283807690","https://openalex.org/W2412182992","https://openalex.org/W2657631929","https://openalex.org/W2685500084","https://openalex.org/W2766099653","https://openalex.org/W2885942546","https://openalex.org/W2914578694","https://openalex.org/W2953256123","https://openalex.org/W2963592159","https://openalex.org/W2991662182","https://openalex.org/W3044852321","https://openalex.org/W3090344101","https://openalex.org/W3094102291","https://openalex.org/W3103869760","https://openalex.org/W3133715412","https://openalex.org/W3199698735","https://openalex.org/W3216643960"],"related_works":["https://openalex.org/W2115729631","https://openalex.org/W2734484373","https://openalex.org/W2552674092","https://openalex.org/W2938845360","https://openalex.org/W2809809406","https://openalex.org/W2950006253","https://openalex.org/W2946632748","https://openalex.org/W2978664318","https://openalex.org/W2897057060","https://openalex.org/W2572994517","https://openalex.org/W3164859732","https://openalex.org/W2296416274","https://openalex.org/W3034493808","https://openalex.org/W2560999006","https://openalex.org/W2951453682","https://openalex.org/W2964139431","https://openalex.org/W2979920384","https://openalex.org/W2750615509","https://openalex.org/W2963914772","https://openalex.org/W71150983"],"abstract_inverted_index":{"In":[0],"this":[1,29],"paper":[2],"we":[3,53],"apply":[4],"the":[5,12,32,35,38,42,45,49,67,70,74],"previously":[6],"introduced":[7],"approximation":[8],"method":[9,30,75],"based":[10],"on":[11,79],"ANOVA":[13],"(analysis":[14],"of":[15,28,34,44,69],"variance)":[16],"decomposition":[17],"and":[18,23,65],"Grouped":[19],"Transformations":[20],"to":[21,40,56,61,76],"synthetic":[22],"real":[24],"data.":[25],"The":[26],"advantage":[27],"is":[31],"interpretability":[33],"approximation,":[36],"i.e.,":[37],"ability":[39],"rank":[41],"importance":[43],"attribute":[46,59],"interactions":[47],"or":[48],"variable":[50],"couplings.":[51],"Moreover,":[52],"are":[54],"able":[55],"generate":[57],"an":[58],"ranking":[60],"identify":[62],"unimportant":[63],"variables":[64],"reduce":[66],"dimensionality":[68],"problem.":[71],"We":[72],"compare":[73],"other":[77],"approaches":[78],"publicly":[80],"available":[81],"benchmark":[82],"datasets.":[83]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
