{"id":"https://openalex.org/W3177463154","doi":"https://doi.org/10.1109/tsp.2022.3158422","title":"Low-Rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation","display_name":"Low-Rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W3177463154","doi":"https://doi.org/10.1109/tsp.2022.3158422","mag":"3177463154"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2022.3158422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3158422","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2106.10591","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078368845","display_name":"Magda Amiridi","orcid":"https://orcid.org/0000-0001-7563-1673"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Magda Amiridi","raw_affiliation_strings":["Department of ECE, University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"Department of ECE, University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077063387","display_name":"Nikos Kargas","orcid":"https://orcid.org/0000-0002-3798-2875"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]},{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nikos Kargas","raw_affiliation_strings":["Department of ECE, University of Minnesota, Charlottesville, VA, USA","Amazon, Cambridge, U.K"],"affiliations":[{"raw_affiliation_string":"Department of ECE, University of Minnesota, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381","https://openalex.org/I130238516"]},{"raw_affiliation_string":"Amazon, Cambridge, U.K","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050186120","display_name":"Nicholas D. Sidiropoulos","orcid":"https://orcid.org/0000-0002-3385-7911"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nicholas D. Sidiropoulos","raw_affiliation_strings":["Department of ECE, University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"Department of ECE, University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078368845"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":1.0944,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75539568,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"70","issue":null,"first_page":"2669","last_page":"2680"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6322906017303467},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.5938807129859924},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.55989009141922},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5204710960388184},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5012905597686768},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.47209328413009644},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4656945466995239},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46093928813934326},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44163960218429565},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42096686363220215},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3530096411705017},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1685725748538971},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12848743796348572}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6322906017303467},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.5938807129859924},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.55989009141922},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5204710960388184},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5012905597686768},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.47209328413009644},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4656945466995239},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46093928813934326},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44163960218429565},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42096686363220215},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3530096411705017},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1685725748538971},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12848743796348572}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2022.3158422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3158422","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2106.10591","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.10591","pdf_url":"https://arxiv.org/pdf/2106.10591","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2106.10591","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.10591","pdf_url":"https://arxiv.org/pdf/2106.10591","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W611367071","https://openalex.org/W1522301498","https://openalex.org/W1583912456","https://openalex.org/W1665214252","https://openalex.org/W1866230956","https://openalex.org/W1959608418","https://openalex.org/W2013807373","https://openalex.org/W2081933490","https://openalex.org/W2099471712","https://openalex.org/W2112796928","https://openalex.org/W2121739212","https://openalex.org/W2129905273","https://openalex.org/W2140574335","https://openalex.org/W2258054274","https://openalex.org/W2267126114","https://openalex.org/W2469230926","https://openalex.org/W2519091744","https://openalex.org/W2618530766","https://openalex.org/W2750384547","https://openalex.org/W2774921216","https://openalex.org/W2786088545","https://openalex.org/W2788017640","https://openalex.org/W2911290044","https://openalex.org/W2912298597","https://openalex.org/W2914578694","https://openalex.org/W2949382160","https://openalex.org/W2952838738","https://openalex.org/W2953318193","https://openalex.org/W2962695743","https://openalex.org/W2962820504","https://openalex.org/W2963047245","https://openalex.org/W2963090522","https://openalex.org/W2963139417","https://openalex.org/W2963223306","https://openalex.org/W2964121744","https://openalex.org/W3009014396","https://openalex.org/W3010559047","https://openalex.org/W3081716549","https://openalex.org/W4207001967","https://openalex.org/W4235649184","https://openalex.org/W4248721357","https://openalex.org/W4289761690","https://openalex.org/W4293849739","https://openalex.org/W4297728530","https://openalex.org/W4297798428","https://openalex.org/W4299046751","https://openalex.org/W4320013936","https://openalex.org/W6610566761","https://openalex.org/W6631190155","https://openalex.org/W6635084905","https://openalex.org/W6637242042","https://openalex.org/W6639317949","https://openalex.org/W6640963894","https://openalex.org/W6680537413","https://openalex.org/W6688384872","https://openalex.org/W6714644935","https://openalex.org/W6738536549","https://openalex.org/W6743688258","https://openalex.org/W6748102297","https://openalex.org/W6748499346","https://openalex.org/W6752910514","https://openalex.org/W6758706709","https://openalex.org/W6758800702","https://openalex.org/W6774552699","https://openalex.org/W6782159035"],"related_works":["https://openalex.org/W4310873165","https://openalex.org/W2355395139","https://openalex.org/W4285596704","https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2988134182","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125"],"abstract_inverted_index":{"Learning":[0],"generative":[1],"probabilistic":[2],"models":[3],"is":[4,57,107,118,135],"a":[5,27,37,61,74,83,138,145],"core":[6],"problem":[7],"in":[8,71,98,122,151],"machine":[9],"learning,":[10],"which":[11,140],"presents":[12],"significant":[13],"challenges":[14],"due":[15],"to":[16,58,66,113],"the":[17,44,52,68,91,95,99,114,128,131,148,152,156,163],"<italic":[18,84,119],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[19,85,120],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">curse":[20],"of":[21,47,51,73,77,90,94,127,147],"dimensionality</i>":[22],".":[23],"This":[24],"paper":[25],"proposes":[26],"joint":[28,92],"dimensionality":[29,63],"reduction":[30],"and":[31,81,109,130,155,173,180],"non-parametric":[32,108],"density":[33,105,133],"estimation":[34],"framework,":[35],"using":[36],"novel":[38],"estimator":[39,134],"that":[40,117,162],"can":[41],"explicitly":[42],"capture":[43],"underlying":[45],"distribution":[46,93],"appropriate":[48],"reduced-dimension":[49],"representations":[50],"input":[53],"data.":[54],"The":[55,102],"idea":[56],"jointly":[59],"design":[60],"nonlinear":[62],"reducing":[64],"auto-encoder":[65,129,157],"model":[67,89,106,165],"training":[69],"data":[70],"terms":[72],"parsimonious":[75],"set":[76],"latent":[78,96,104,132,153],"random":[79],"variables,":[80],"learn":[82],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">canonical</i>":[86],"low-rank":[87],"tensor":[88],"variables":[97],"Fourier":[100],"domain.":[101],"proposed":[103,164],"\u201cuniversal,\u201d":[110],"as":[111],"opposed":[112],"predefined":[115],"prior":[116],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">assumed</i>":[121],"variational":[123],"auto-encoders.":[124],"Joint":[125],"optimization":[126],"pursued":[136],"via":[137],"formulation":[139],"learns":[141],"both":[142],"by":[143],"minimizing":[144],"combination":[146],"negative":[149],"log-likelihood":[150],"domain":[154],"reconstruction":[158],"loss.":[159],"We":[160],"demonstrate":[161],"achieves":[166],"very":[167],"promising":[168],"results":[169],"on":[170,176],"toy,":[171],"tabular,":[172],"image":[174],"datasets":[175],"regression":[177],"tasks,":[178],"sampling,":[179],"anomaly":[181],"detection.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2021-07-05T00:00:00"}