{"id":"https://openalex.org/W2104090753","doi":"https://doi.org/10.1109/tnnls.2013.2284818","title":"A Robust Subspace Projection Autoassociative Memory Based on the M-Estimation Method","display_name":"A Robust Subspace Projection Autoassociative Memory Based on the M-Estimation Method","publication_year":2013,"publication_date":"2013-10-18","ids":{"openalex":"https://openalex.org/W2104090753","doi":"https://doi.org/10.1109/tnnls.2013.2284818","mag":"2104090753"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2013.2284818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2013.2284818","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-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/A5012009095","display_name":"Marcos Eduardo Valle","orcid":"https://orcid.org/0000-0003-4026-5110"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Marcos Eduardo Valle","raw_affiliation_strings":["Department of Applied Mathematics, University of Campinas, Campinas, Brazil","Department of Applied Mathematics , University of Campinas, Campinas, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]},{"raw_affiliation_string":"Department of Applied Mathematics , University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5012009095"],"corresponding_institution_ids":["https://openalex.org/I181391015"],"apc_list":null,"apc_paid":null,"fwci":4.8677,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.94984207,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"25","issue":"7","first_page":"1372","last_page":"1377"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9940999746322632,"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"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9915000200271606,"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/subspace-topology","display_name":"Subspace topology","score":0.7982882857322693},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.734102725982666},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6411770582199097},{"id":"https://openalex.org/keywords/content-addressable-memory","display_name":"Content-addressable memory","score":0.6371440887451172},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.541368842124939},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5397075414657593},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.533429741859436},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5308918356895447},{"id":"https://openalex.org/keywords/bidirectional-associative-memory","display_name":"Bidirectional associative memory","score":0.453681617975235},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4248216152191162},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32720208168029785},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2053496241569519}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7982882857322693},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.734102725982666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6411770582199097},{"id":"https://openalex.org/C53442348","wikidata":"https://www.wikidata.org/wiki/Q745101","display_name":"Content-addressable memory","level":3,"score":0.6371440887451172},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.541368842124939},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5397075414657593},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.533429741859436},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5308918356895447},{"id":"https://openalex.org/C120620853","wikidata":"https://www.wikidata.org/wiki/Q506355","display_name":"Bidirectional associative memory","level":4,"score":0.453681617975235},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4248216152191162},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32720208168029785},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2053496241569519}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnnls.2013.2284818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2013.2284818","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5106750484","display_name":null,"funder_award_id":"304240/2011-7","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"},{"id":"https://openalex.org/G7945038680","display_name":null,"funder_award_id":"2013/12310-4","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"}],"funders":[{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W51874640","https://openalex.org/W137035276","https://openalex.org/W1493971325","https://openalex.org/W1597286183","https://openalex.org/W1992434499","https://openalex.org/W2018868680","https://openalex.org/W2050551672","https://openalex.org/W2054113207","https://openalex.org/W2078102483","https://openalex.org/W2097184312","https://openalex.org/W2132672282","https://openalex.org/W2140235830","https://openalex.org/W2152701363","https://openalex.org/W2153342234","https://openalex.org/W2168665295","https://openalex.org/W2169006540","https://openalex.org/W2478108171","https://openalex.org/W2489822048","https://openalex.org/W3036512766","https://openalex.org/W4242010931","https://openalex.org/W4245176872","https://openalex.org/W4251766404","https://openalex.org/W4253735042","https://openalex.org/W4255230573","https://openalex.org/W6602138691"],"related_works":["https://openalex.org/W1987836535","https://openalex.org/W4321273133","https://openalex.org/W2109892802","https://openalex.org/W1503438809","https://openalex.org/W2769111744","https://openalex.org/W1536116856","https://openalex.org/W2260028708","https://openalex.org/W2147932521","https://openalex.org/W2120558138","https://openalex.org/W1637798125"],"abstract_inverted_index":{"An":[0],"autoassociative":[1],"memory":[2,68],"(AM)":[3],"that":[4,99],"projects":[5],"an":[6,40,104],"input":[7,126],"pattern":[8],"onto":[9],"a":[10,17,49,74],"linear":[11,24],"subspace":[12,18],"is":[13,39],"referred":[14],"to":[15,61,109,121],"as":[16,114,116],"projection":[19],"AM":[20,25],"(SPAM).":[21],"The":[22],"optimal":[23],"(OLAM),":[26],"which":[27,78],"can":[28],"be":[29],"used":[30],"for":[31],"the":[32,55,62,70,79,86,92,100],"storage":[33],"and":[34,64,111,124],"recall":[35],"of":[36,42,94],"real-valued":[37],"patterns,":[38],"example":[41],"SPAM.":[43],"In":[44,59],"this":[45],"brief":[46],"we":[47],"introduce":[48],"novel":[50,101],"SPAM":[51,72],"model":[52],"based":[53],"on":[54],"robust":[56,71],"M-estimation":[57],"method.":[58],"contrast":[60],"OLAM":[63],"many":[65],"other":[66],"associative":[67],"models,":[69],"represents":[73],"neural":[75],"network":[76],"in":[77],"synaptic":[80],"weights":[81],"are":[82],"iteratively":[83],"adjusted":[84],"during":[85],"retrieval":[87],"phase.":[88],"Computational":[89],"experiments":[90],"concerning":[91],"reconstruction":[93],"corrupted":[95],"gray-scale":[96],"images":[97],"reveal":[98],"memories":[102],"exhibit":[103],"excellent":[105],"tolerance":[106,118],"with":[107,119],"respect":[108,120],"salt":[110],"pepper":[112],"noise":[113,123],"well":[115],"some":[117],"Gaussian":[122],"blurred":[125],"images.":[127]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}