{"id":"https://openalex.org/W2896996515","doi":"https://doi.org/10.1109/ijcnn.2018.8489050","title":"A Neural Net Framework for Accumulative Feature-based Matrix Completion","display_name":"A Neural Net Framework for Accumulative Feature-based Matrix Completion","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2896996515","doi":"https://doi.org/10.1109/ijcnn.2018.8489050","mag":"2896996515"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489050","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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/A5087399189","display_name":"Mehmet Aktukmak","orcid":"https://orcid.org/0000-0001-5669-7749"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mehmet Aktukmak","raw_affiliation_strings":["Electrical Engineering Department, University of South Florida, Tampa, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, University of South Florida, Tampa, US","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089703583","display_name":"Samuel Mercier","orcid":"https://orcid.org/0000-0003-4912-8665"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Samuel Mercier","raw_affiliation_strings":["Electrical Engineering Department, University of South Florida, Tampa, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, University of South Florida, Tampa, US","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103169824","display_name":"Ismail Uysal","orcid":"https://orcid.org/0000-0002-3224-4865"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ismail Uysal","raw_affiliation_strings":["Electrical Engineering Department, University of South Florida, Tampa, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, University of South Florida, Tampa, US","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2613432"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.992900013923645,"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/T10320","display_name":"Neural Networks and Applications","score":0.9894000291824341,"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/computer-science","display_name":"Computer science","score":0.7460063695907593},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7173702716827393},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.691941499710083},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.6447239518165588},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.569524884223938},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5452762246131897},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5165568590164185},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4499361515045166},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4460344612598419},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4362753629684448},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35698240995407104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7460063695907593},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7173702716827393},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.691941499710083},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.6447239518165588},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.569524884223938},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5452762246131897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5165568590164185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4499361515045166},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4460344612598419},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4362753629684448},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35698240995407104},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489050","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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":21,"referenced_works":["https://openalex.org/W1720514416","https://openalex.org/W1920010643","https://openalex.org/W1968213687","https://openalex.org/W1972832829","https://openalex.org/W1982142851","https://openalex.org/W2016515835","https://openalex.org/W2020641160","https://openalex.org/W2056115455","https://openalex.org/W2078339956","https://openalex.org/W2094589433","https://openalex.org/W2117116150","https://openalex.org/W2128330514","https://openalex.org/W2134296096","https://openalex.org/W2145187524","https://openalex.org/W2610998635","https://openalex.org/W2620814161","https://openalex.org/W3083577026","https://openalex.org/W3120740533","https://openalex.org/W6640122379","https://openalex.org/W6679352219","https://openalex.org/W6782199257"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W1977098485","https://openalex.org/W3103289951","https://openalex.org/W3152273675"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,28,91,149],"novel":[6],"multimodal":[7],"framework":[8],"for":[9,24],"matrix":[10,109],"completion":[11],"where":[12,95],"the":[13,35,39,42,59,69,73,80,112,129,138],"missing":[14,36,74,155],"values":[15,37],"are":[16,54,82],"accumulatively":[17],"estimated":[18],"with":[19,101],"feature-based":[20],"neural":[21],"networks.":[22],"Specifically,":[23],"each":[25,102],"individual":[26],"feature,":[27],"different":[29,152],"model":[30,63,113],"is":[31],"trained":[32],"to":[33,47,57,65,68,90,143],"predict":[34],"in":[38,123,135],"observations":[40],"using":[41],"remaining":[43],"features":[44],"as":[45],"input":[46],"provide":[48],"an":[49],"initial":[50],"estimate.":[51],"These":[52],"estimates":[53],"then":[55],"used":[56],"initiate":[58],"next":[60],"round":[61],"of":[62,72,79,137,151],"training":[64,96],"iteratively":[66],"converge":[67],"final":[70],"prediction":[71],"value.":[75],"The":[76],"weight":[77],"parameters":[78],"networks":[81],"propagated":[83],"through":[84],"these":[85],"accumulative":[86],"iterations":[87],"which":[88],"leads":[89],"computationally":[92],"efficient":[93],"algorithm":[94,131],"times":[97],"become":[98],"progressively":[99],"shorter":[100],"round.":[103],"Unlike":[104],"traditional":[105],"algorithms":[106],"relying":[107],"on":[108,148],"factorization,":[110],"separating":[111],"building":[114],"and":[115,154],"exploiting":[116],"steps":[117],"also":[118],"enables":[119],"more":[120],"effective":[121],"deployment":[122],"online":[124],"applications.":[125],"Results":[126],"show":[127],"that":[128],"proposed":[130],"outperforms":[132],"prior":[133],"work":[134],"80%":[136],"test":[139],"scenarios":[140],"when":[141],"compared":[142],"four":[144],"universally":[145],"accepted":[146],"methods":[147],"combination":[150],"datasets":[153],"data":[156],"ratios.":[157]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
