{"id":"https://openalex.org/W4310873165","doi":"https://doi.org/10.1109/metroxraine54828.2022.9967686","title":"From Principal Component Analysis to Autoencoders: a comparison on simulated data from psychometric models","display_name":"From Principal Component Analysis to Autoencoders: a comparison on simulated data from psychometric models","publication_year":2022,"publication_date":"2022-10-26","ids":{"openalex":"https://openalex.org/W4310873165","doi":"https://doi.org/10.1109/metroxraine54828.2022.9967686"},"language":"en","primary_location":{"id":"doi:10.1109/metroxraine54828.2022.9967686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroxraine54828.2022.9967686","pdf_url":null,"source":{"id":"https://openalex.org/S4363608343","display_name":"2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5057751580","display_name":"Monica Casella","orcid":"https://orcid.org/0000-0002-6017-602X"},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Monica Casella","raw_affiliation_strings":["University of Naples Federico II,Naples,Italy","University of Naples Federico II, Naples, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Naples Federico II,Naples,Italy","institution_ids":["https://openalex.org/I71267560"]},{"raw_affiliation_string":"University of Naples Federico II, Naples, Italy","institution_ids":["https://openalex.org/I71267560"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003724609","display_name":"Pasquale Dolce","orcid":"https://orcid.org/0000-0002-7588-6067"},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Pasquale Dolce","raw_affiliation_strings":["University of Naples Federico II,Naples,Italy","University of Naples Federico II, Naples, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Naples Federico II,Naples,Italy","institution_ids":["https://openalex.org/I71267560"]},{"raw_affiliation_string":"University of Naples Federico II, Naples, Italy","institution_ids":["https://openalex.org/I71267560"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052161872","display_name":"Michela Ponticorvo","orcid":"https://orcid.org/0000-0003-2451-9539"},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Michela Ponticorvo","raw_affiliation_strings":["University of Naples Federico II,Naples,Italy","University of Naples Federico II, Naples, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Naples Federico II,Naples,Italy","institution_ids":["https://openalex.org/I71267560"]},{"raw_affiliation_string":"University of Naples Federico II, Naples, Italy","institution_ids":["https://openalex.org/I71267560"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027259197","display_name":"\u200eDavide Marocco","orcid":"https://orcid.org/0000-0001-5185-1313"},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Davide Marocco","raw_affiliation_strings":["University of Naples Federico II,Naples,Italy","University of Naples Federico II, Naples, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Naples Federico II,Naples,Italy","institution_ids":["https://openalex.org/I71267560"]},{"raw_affiliation_string":"University of Naples Federico II, Naples, Italy","institution_ids":["https://openalex.org/I71267560"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I71267560"],"apc_list":null,"apc_paid":null,"fwci":2.3484,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.89608541,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"377","last_page":"381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9786999821662903,"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/autoencoder","display_name":"Autoencoder","score":0.965479850769043},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.9248687028884888},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.8773242235183716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7536331415176392},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7131952047348022},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6316044926643372},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6278382539749146},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6046565771102905},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.424710214138031},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3726081848144531}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.965479850769043},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.9248687028884888},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.8773242235183716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7536331415176392},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7131952047348022},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6316044926643372},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6278382539749146},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6046565771102905},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.424710214138031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3726081848144531}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/metroxraine54828.2022.9967686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroxraine54828.2022.9967686","pdf_url":null,"source":{"id":"https://openalex.org/S4363608343","display_name":"2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1913957972","https://openalex.org/W1931276471","https://openalex.org/W1959608418","https://openalex.org/W1981788861","https://openalex.org/W1995563977","https://openalex.org/W2017257315","https://openalex.org/W2071128523","https://openalex.org/W2078626246","https://openalex.org/W2097665571","https://openalex.org/W2131329059","https://openalex.org/W2137570937","https://openalex.org/W2163922914","https://openalex.org/W2294798173","https://openalex.org/W2766445325","https://openalex.org/W3013921559","https://openalex.org/W3013927267","https://openalex.org/W3024333932","https://openalex.org/W3035198795","https://openalex.org/W3047415577","https://openalex.org/W3099514962","https://openalex.org/W3165135914","https://openalex.org/W4212863985","https://openalex.org/W4244323965","https://openalex.org/W4295312788","https://openalex.org/W6640963894","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2579148721","https://openalex.org/W2669956259","https://openalex.org/W4387893611","https://openalex.org/W2347335694","https://openalex.org/W2091056927","https://openalex.org/W2067407580","https://openalex.org/W4317486777","https://openalex.org/W4389669152","https://openalex.org/W4249005693","https://openalex.org/W4310873165"],"abstract_inverted_index":{"Dimensionality":[0],"reduction":[1,29,164],"is":[2,22,122],"the":[3,11,14,25],"search":[4],"for":[5,33,85],"a":[6,50,71,108,110,132],"low-dimensional":[7],"space":[8],"that":[9,143,152],"captures":[10],"\u201cessence\u201d":[12],"of":[13,24,74,119,167],"original":[15],"high-dimensional":[16],"data.":[17],"Principal":[18,136],"Component":[19,137],"Analysis":[20,138],"(PCA)":[21],"one":[23],"most":[26],"used":[27,80,182],"dimensionality":[28,86,163],"techniques":[30,42],"in":[31,81,161,165],"psychology":[32],"data":[34,142,175],"analysis":[35],"and":[36,47,70,93,135,156,169,179],"measure":[37],"development.":[38],"However,":[39],"machine":[40],"learning":[41],"manage":[43],"more":[44],"complex":[45],"data,":[46],"they":[48],"represent":[49],"valuable":[51],"alternative":[52],"to":[53,123],"classical":[54],"methods.":[55],"In":[56,102],"this":[57,120],"work,":[58],"we":[59],"consider":[60],"autoencoders,":[61],"neural":[62,111],"networks":[63],"with":[64,128],"as":[65,68],"many":[66],"inputs":[67],"outputs":[69],"smaller":[72],"number":[73],"hidden":[75,130],"nodes,":[76],"which":[77,113],"are":[78,158],"widely":[79],"several":[82],"study":[83],"fields":[84],"reduction.":[87],"Recent":[88],"literature":[89],"has":[90,106],"compared":[91],"PCA":[92,115,157],"autoencoders":[94,155],"focusing":[95],"especially":[96],"on":[97,140,173],"differences":[98],"concerning":[99],"image":[100],"reconstruction.":[101],"particular,":[103],"recent":[104],"research":[105],"proposed":[107],"PCA-Autoencoder,":[109],"network":[112],"embeds":[114],"properties.":[116],"The":[117],"objective":[118],"work":[121],"compare":[124],"PCA-autoencoder,":[125],"an":[126],"autoencoder":[127,134],"uncorrelated":[129],"features,":[131],"simple":[133],"performances":[139],"artificial":[141],"have":[144],"been":[145],"simulated":[146,174],"from":[147,176],"factor-based":[148],"populations.":[149],"Results":[150],"show":[151,186],"three-layered":[153],"linear":[154],"very":[159],"similar":[160],"performing":[162],"terms":[166],"accuracy":[168],"reconstruction":[170],"error":[171],"also":[172],"psychometric":[177],"models":[178],"could":[180],"be":[181],"where":[183],"traditional":[184],"methods":[185],"their":[187],"limits.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
