{"id":"https://openalex.org/W4308216323","doi":"https://doi.org/10.1109/allerton49937.2022.9929425","title":"Classifying Incomplete Data with a Mixture of Subspace Experts","display_name":"Classifying Incomplete Data with a Mixture of Subspace Experts","publication_year":2022,"publication_date":"2022-09-27","ids":{"openalex":"https://openalex.org/W4308216323","doi":"https://doi.org/10.1109/allerton49937.2022.9929425"},"language":"en","primary_location":{"id":"doi:10.1109/allerton49937.2022.9929425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton49937.2022.9929425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","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/A5080935418","display_name":"Ben A. Kizaric","orcid":null},"institutions":[{"id":"https://openalex.org/I2802999273","display_name":"Wisconsin Institutes for Discovery","ror":"https://ror.org/05gbven85","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2802999273"]},{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ben A. Kizaric","raw_affiliation_strings":["University of Wisconsin-Madison,Department of Electrical and Computer Engineering","Department of Electrical and Computer Engineering, University of Wisconsin-Madison","Wisconsin Institute for Discovery, University of Wisconsin-Madison"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"Wisconsin Institute for Discovery, University of Wisconsin-Madison","institution_ids":["https://openalex.org/I2802999273","https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084976034","display_name":"Daniel Pimentel-Alarc\u00f3n","orcid":"https://orcid.org/0000-0003-0871-7009"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]},{"id":"https://openalex.org/I2802999273","display_name":"Wisconsin Institutes for Discovery","ror":"https://ror.org/05gbven85","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2802999273"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel L. Pimentel-Alarcon","raw_affiliation_strings":["Wisconsin Institute for Discovery, University of Wisconsin-Madison","Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison"],"affiliations":[{"raw_affiliation_string":"Wisconsin Institute for Discovery, University of Wisconsin-Madison","institution_ids":["https://openalex.org/I2802999273","https://openalex.org/I135310074"]},{"raw_affiliation_string":"Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080935418"],"corresponding_institution_ids":["https://openalex.org/I135310074","https://openalex.org/I2802999273"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.61917666,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9987000226974487,"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":0.998199999332428,"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/linear-subspace","display_name":"Linear subspace","score":0.7720592021942139},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.7192270755767822},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7180117964744568},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6717782616615295},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6482976078987122},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6447091102600098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.562609076499939},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5520164370536804},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5035037398338318},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.496568500995636},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4433205723762512},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.44301486015319824},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42702075839042664},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22799783945083618}],"concepts":[{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.7720592021942139},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7192270755767822},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7180117964744568},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6717782616615295},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6482976078987122},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6447091102600098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.562609076499939},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5520164370536804},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5035037398338318},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.496568500995636},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4433205723762512},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.44301486015319824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42702075839042664},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22799783945083618},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/allerton49937.2022.9929425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton49937.2022.9929425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.49000000953674316,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1506622324","https://openalex.org/W1533128638","https://openalex.org/W1550861865","https://openalex.org/W1716124544","https://openalex.org/W1774786334","https://openalex.org/W1781481323","https://openalex.org/W1993962865","https://openalex.org/W2007339694","https://openalex.org/W2025653905","https://openalex.org/W2079875563","https://openalex.org/W2083143867","https://openalex.org/W2103972604","https://openalex.org/W2105234758","https://openalex.org/W2106401878","https://openalex.org/W2113590298","https://openalex.org/W2133105369","https://openalex.org/W2146332392","https://openalex.org/W2150884987","https://openalex.org/W2160152628","https://openalex.org/W2162991150","https://openalex.org/W2324986914","https://openalex.org/W2412510955","https://openalex.org/W2517389697","https://openalex.org/W2750384547","https://openalex.org/W2752794725","https://openalex.org/W2803403013","https://openalex.org/W2810953629","https://openalex.org/W2913929988","https://openalex.org/W2952229419","https://openalex.org/W2962834831","https://openalex.org/W2964010366","https://openalex.org/W2981230733","https://openalex.org/W2984353870","https://openalex.org/W3013950266","https://openalex.org/W3034513922","https://openalex.org/W3035721909","https://openalex.org/W3120740533","https://openalex.org/W3162948016","https://openalex.org/W3170657538","https://openalex.org/W3176247726","https://openalex.org/W4223563429","https://openalex.org/W4288562516","https://openalex.org/W4300187280","https://openalex.org/W4399647672","https://openalex.org/W6623329352","https://openalex.org/W6630218264","https://openalex.org/W6715189028","https://openalex.org/W6743688258","https://openalex.org/W6751145664","https://openalex.org/W6775095138","https://openalex.org/W6797727952","https://openalex.org/W6869608176"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3100286349","https://openalex.org/W3028371478","https://openalex.org/W2081476516"],"abstract_inverted_index":{"Performing":[0],"inference":[1,27],"on":[2,28,77,83,132],"data":[3,53],"with":[4,43,69,121,136],"missing":[5,23,70,122],"values":[6,24,71],"is":[7,18,55,130],"a":[8,95,106,116,140,155,177,193],"highly":[9],"common":[10],"yet":[11],"challenging":[12],"task":[13],"in":[14,46],"machine":[15],"learning,":[16],"and":[17],"often":[19],"addressed":[20],"by":[21,170,176],"imputing":[22],"before":[25],"performing":[26],"the":[29,33,80,87,133,162,171,186],"filled":[30],"dataset.":[31],"However,":[32],"two":[34],"parts":[35],"of":[36,79,86,108,113,119,143,157,179,189,195],"this":[37,60,190],"paradigm":[38],"may":[39,50],"be":[40],"at":[41],"odds":[42],"one":[44],"another":[45],"that":[47,54],"optimal":[48,56],"imputation":[49],"not":[51,82],"yield":[52],"for":[57,127],"inference.":[58],"In":[59],"paper,":[61],"we":[62],"propose":[63],"an":[64,102,125],"ensemble":[65],"approach":[66],"to":[67,149,154,164],"classification":[68],"where":[72],"base":[73],"learners":[74],"are":[75,152,168,174],"trained":[76,131],"embeddings":[78],"data,":[81,158],"imputed":[84],"versions":[85],"data.":[88,123],"To":[89],"accomplish":[90],"this,":[91],"MoSEs":[92],"first":[93],"uses":[94],"novel":[96],"subspace":[97,129,156],"clustering":[98],"algorithm":[99],"which":[100,114,150,165],"characterizes":[101],"input":[103],"space":[104],"as":[105,159,161],"hierarchy":[107],"localized":[109,153],"affine":[110],"subspaces,":[111],"each":[112,128],"produces":[115],"low-dimensional":[117],"embedding":[118],"samples":[120],"Then,":[124],"expert":[126,145],"associated":[134],"embedding,":[135],"final":[137,166],"predictions":[138,167],"being":[139],"weighted":[141],"sum":[142],"all":[144],"predictions.":[146],"The":[147],"degree":[148,163],"experts":[151],"well":[160],"influenced":[169],"nearest":[172],"subspaces":[173],"controlled":[175],"pair":[178],"tunable":[180],"hyper-parameters.":[181],"We":[182],"perform":[183],"experiments":[184],"demonstrating":[185],"state-of-the-art":[187],"performance":[188],"method":[191],"across":[192],"variety":[194],"reference":[196],"datasets.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}