{"id":"https://openalex.org/W4391420769","doi":"https://doi.org/10.1109/access.2024.3443646","title":"A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data","display_name":"A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391420769","doi":"https://doi.org/10.1109/access.2024.3443646"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3443646","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3443646","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3443646","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001235097","display_name":"Navid Ziaei","orcid":"https://orcid.org/0000-0001-5834-6248"},"institutions":[{"id":"https://openalex.org/I170013655","display_name":"Isfahan University of Technology","ror":"https://ror.org/00af3sa43","country_code":"IR","type":"education","lineage":["https://openalex.org/I170013655"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Navid Ziaei","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran"],"raw_orcid":"https://orcid.org/0000-0001-5834-6248","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran","institution_ids":["https://openalex.org/I170013655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101589138","display_name":"Behzad Nazari","orcid":"https://orcid.org/0000-0001-7525-5014"},"institutions":[{"id":"https://openalex.org/I170013655","display_name":"Isfahan University of Technology","ror":"https://ror.org/00af3sa43","country_code":"IR","type":"education","lineage":["https://openalex.org/I170013655"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Behzad Nazari","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran"],"raw_orcid":"https://orcid.org/0000-0001-7525-5014","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran","institution_ids":["https://openalex.org/I170013655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004106989","display_name":"Uri T. Eden","orcid":"https://orcid.org/0000-0002-2058-3691"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Uri T. Eden","raw_affiliation_strings":["Department of Mathematics and Statistics, Boston University, Boston, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Boston University, Boston, MA, USA","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004461160","display_name":"Alik S. Widge","orcid":"https://orcid.org/0000-0001-8510-341X"},"institutions":[{"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":"Alik S. Widge","raw_affiliation_strings":["Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":"https://orcid.org/0000-0001-8510-341X","affiliations":[{"raw_affiliation_string":"Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101895582","display_name":"Ali Yousefi","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Yousefi","raw_affiliation_strings":["Department of Biomedical Engineering, University of Houston, Houston, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.5273,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.84244253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"12","issue":null,"first_page":"113314","last_page":"113335"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9589999914169312,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9589999914169312,"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/latent-variable","display_name":"Latent variable","score":0.8230533003807068},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.763141930103302},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6010007262229919},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.5705652236938477},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5394284725189209},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.48466020822525024},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.451588898897171},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.4475199580192566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4325387477874756},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.34828293323516846},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3416610360145569},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.33015182614326477},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31498461961746216},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29319828748703003},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08806687593460083}],"concepts":[{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.8230533003807068},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.763141930103302},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6010007262229919},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.5705652236938477},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5394284725189209},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.48466020822525024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.451588898897171},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.4475199580192566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4325387477874756},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.34828293323516846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3416610360145569},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.33015182614326477},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31498461961746216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29319828748703003},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08806687593460083},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2024.3443646","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3443646","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2401.16497","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.16497","pdf_url":"https://arxiv.org/pdf/2401.16497","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:doaj.org/article:f42826521187497fa4a7a00052a4dd55","is_oa":true,"landing_page_url":"https://doaj.org/article/f42826521187497fa4a7a00052a4dd55","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 113314-113335 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3443646","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3443646","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1994005439","https://openalex.org/W2007339694","https://openalex.org/W2012725996","https://openalex.org/W2017539895","https://openalex.org/W2041657594","https://openalex.org/W2042970394","https://openalex.org/W2050654176","https://openalex.org/W2083196737","https://openalex.org/W2103731208","https://openalex.org/W2108193616","https://openalex.org/W2125027820","https://openalex.org/W2128040385","https://openalex.org/W2130416410","https://openalex.org/W2225156818","https://openalex.org/W2523498403","https://openalex.org/W2763148304","https://openalex.org/W2781973163","https://openalex.org/W2793806292","https://openalex.org/W2796140331","https://openalex.org/W2944851425","https://openalex.org/W2947137296","https://openalex.org/W2948978827","https://openalex.org/W2963608065","https://openalex.org/W2979583955","https://openalex.org/W2990621560","https://openalex.org/W2997325095","https://openalex.org/W3024333932","https://openalex.org/W3147919432","https://openalex.org/W4206639844","https://openalex.org/W4286490395","https://openalex.org/W4311415873","https://openalex.org/W4313332870","https://openalex.org/W4386687412","https://openalex.org/W4389542918","https://openalex.org/W6602631663","https://openalex.org/W6605566567","https://openalex.org/W6609126827","https://openalex.org/W6631732945","https://openalex.org/W6637968757","https://openalex.org/W6674989108","https://openalex.org/W6676930901","https://openalex.org/W6679242185","https://openalex.org/W6680375596","https://openalex.org/W6684785420","https://openalex.org/W6738492492","https://openalex.org/W6748816842","https://openalex.org/W6755463424","https://openalex.org/W6756040250","https://openalex.org/W6859298233","https://openalex.org/W6929374308"],"related_works":["https://openalex.org/W4230230730","https://openalex.org/W1501016332","https://openalex.org/W4237379778","https://openalex.org/W1535265092","https://openalex.org/W1603253275","https://openalex.org/W4381250654","https://openalex.org/W2103023456","https://openalex.org/W1565287552","https://openalex.org/W2363394205","https://openalex.org/W3109783536"],"abstract_inverted_index":{"Extracting":[0],"meaningful":[1],"information":[2],"from":[3],"high-dimensional":[4,39,175,194],"data":[5,14,40,61,114,166,176],"poses":[6],"a":[7,27,45],"formidable":[8],"modeling":[9,30,190],"challenge,":[10],"particularly":[11],"when":[12],"the":[13,33,52,60,66,77,88,106,121,131,185],"is":[15,73,168],"obscured":[16],"by":[17,41],"noise":[18],"or":[19],"represented":[20],"through":[21],"different":[22],"modalities.":[23],"This":[24,49],"research":[25],"proposes":[26],"novel":[28],"non-parametric":[29,189],"approach,":[31],"leveraging":[32],"Gaussian":[34,135],"process":[35],"(GP),":[36],"to":[37,44,75,82,129,173,192],"characterize":[38,174],"mapping":[42],"it":[43],"latent":[46,53,78],"low-dimensional":[47],"manifold.":[48],"model,":[50],"named":[51],"discriminative":[54],"generative":[55],"decoder":[56],"(LDGD),":[57],"employs":[58],"both":[59,96],"and":[62,98,147,158],"associated":[63],"labels":[64,116,161],"in":[65,87,112,164],"manifold":[67,107,157],"discovery":[68],"process.":[69],"A":[70],"Bayesian":[71],"solution":[72],"derived":[74],"infer":[76,105,156],"variables,":[79],"allowing":[80],"LDGD":[81,92,104,153],"effectively":[83],"capture":[84],"inherent":[85],"stochasticity":[86],"data.":[89,195],"Applications":[90],"of":[91,123,134,187],"are":[93,127],"demonstrated":[94],"on":[95],"synthetic":[97],"benchmark":[99],"datasets.":[100],"Not":[101],"only":[102],"does":[103],"accurately,":[108],"but":[109],"its":[110,171],"accuracy":[111],"predicting":[113],"points\u2019":[115],"surpasses":[117],"state-of-the-art":[118],"approaches.":[119],"In":[120],"development":[122],"LDGD,":[124],"inducing":[125],"points":[126],"incorporated":[128],"reduce":[130],"computational":[132],"complexity":[133],"processes":[136],"for":[137,143,162],"large":[138],"datasets,":[139],"enabling":[140],"batch":[141],"training":[142],"enhanced":[144],"efficient":[145],"processing":[146],"scalability.":[148],"Additionally,":[149],"we":[150],"show":[151],"that":[152],"can":[154],"robustly":[155],"precisely":[159],"predict":[160],"scenarios":[163],"which":[165],"size":[167],"limited,":[169],"demonstrating":[170],"capability":[172],"with":[177],"limited":[178],"samples":[179],"efficiently.":[180],"These":[181],"collective":[182],"attributes":[183],"highlight":[184],"importance":[186],"developing":[188],"approaches":[191],"analyze":[193]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}