{"id":"https://openalex.org/W2922377980","doi":"https://doi.org/10.23919/apsipa.2018.8659560","title":"Block Tensor Train Decomposition for Missing Value Imputation","display_name":"Block Tensor Train Decomposition for Missing Value Imputation","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2922377980","doi":"https://doi.org/10.23919/apsipa.2018.8659560","mag":"2922377980"},"language":"en","primary_location":{"id":"doi:10.23919/apsipa.2018.8659560","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659560","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-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/A5042263650","display_name":"Namgil Lee","orcid":"https://orcid.org/0000-0003-0593-9028"},"institutions":[{"id":"https://openalex.org/I165507594","display_name":"Kangwon National University","ror":"https://ror.org/01mh5ph17","country_code":"KR","type":"education","lineage":["https://openalex.org/I165507594"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Namgil Lee","raw_affiliation_strings":["Kangwon National University, Chuncheon, South Korea"],"affiliations":[{"raw_affiliation_string":"Kangwon National University, Chuncheon, South Korea","institution_ids":["https://openalex.org/I165507594"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5042263650"],"corresponding_institution_ids":["https://openalex.org/I165507594"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13706294,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1338","last_page":"1343"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9772999882698059,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.9086893200874329},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7336102724075317},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.667471170425415},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6084607243537903},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5804059505462646},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5607260465621948},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.49038243293762207},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4700084328651428},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46196550130844116},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46132534742355347},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46047061681747437},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.41711705923080444},{"id":"https://openalex.org/keywords/singular-value","display_name":"Singular value","score":0.41492316126823425},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.412270188331604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35342341661453247},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2681099474430084},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24034947156906128},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.14047756791114807},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07761582732200623},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06413385272026062},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06263896822929382},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.06227385997772217}],"concepts":[{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.9086893200874329},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7336102724075317},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.667471170425415},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6084607243537903},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5804059505462646},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5607260465621948},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.49038243293762207},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4700084328651428},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46196550130844116},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46132534742355347},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46047061681747437},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.41711705923080444},{"id":"https://openalex.org/C109282560","wikidata":"https://www.wikidata.org/wiki/Q4166054","display_name":"Singular value","level":3,"score":0.41492316126823425},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.412270188331604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35342341661453247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2681099474430084},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24034947156906128},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.14047756791114807},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07761582732200623},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06413385272026062},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06263896822929382},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.06227385997772217},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/apsipa.2018.8659560","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659560","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/W1412447802","https://openalex.org/W1531635645","https://openalex.org/W1538092215","https://openalex.org/W1597703625","https://openalex.org/W1967077133","https://openalex.org/W1989786408","https://openalex.org/W1993482030","https://openalex.org/W2008657736","https://openalex.org/W2024165284","https://openalex.org/W2032997274","https://openalex.org/W2036133196","https://openalex.org/W2054141820","https://openalex.org/W2063125621","https://openalex.org/W2146130798","https://openalex.org/W2480854438","https://openalex.org/W2593392256","https://openalex.org/W2737525247","https://openalex.org/W2963484322","https://openalex.org/W3003365835","https://openalex.org/W3105925603","https://openalex.org/W6682042988"],"related_works":["https://openalex.org/W4319586039","https://openalex.org/W4382583540","https://openalex.org/W1607100495","https://openalex.org/W2010100052","https://openalex.org/W2148568324","https://openalex.org/W1990844505","https://openalex.org/W4378770618","https://openalex.org/W4386721910","https://openalex.org/W2038393145","https://openalex.org/W2059545631"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,17,61,72,88,97],"new":[3],"method":[4,36,85],"for":[5,66],"imputation":[6],"of":[7,45,55,91],"missing":[8,51,92],"values":[9,93],"in":[10],"large":[11,67,89],"scale":[12,68],"matrix":[13,49],"data":[14,32,48,69,80],"based":[15,59],"on":[16,60,78],"low-rank":[18,62,73],"tensor":[19,25,74],"approximation":[20],"technique":[21],"called":[22],"the":[23,34,39,46,83],"block":[24,63],"train":[26],"(TT)":[27],"decomposition.":[28],"Given":[29],"sparsely":[30],"observed":[31],"points,":[33],"proposed":[35,84],"iteratively":[37],"computes":[38],"soft-thresholded":[40],"singular":[41],"value":[42],"decomposition":[43,65],"(SVD)":[44],"underlying":[47],"with":[50,71],"values.":[52],"The":[53],"SVD":[54],"matrices":[56,70],"is":[57],"performed":[58],"TT":[64],"structure.":[75],"Experimental":[76],"results":[77],"simulated":[79],"demonstrate":[81],"that":[82],"can":[86],"estimate":[87],"amount":[90],"accurately":[94],"compared":[95],"to":[96],"matrix-based":[98],"standard":[99],"method.":[100]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}