{"id":"https://openalex.org/W2744088567","doi":"https://doi.org/10.1109/isit.2017.8006584","title":"A characterization of sampling patterns for low-tucker-rank tensor completion problem","display_name":"A characterization of sampling patterns for low-tucker-rank tensor completion problem","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2744088567","doi":"https://doi.org/10.1109/isit.2017.8006584","mag":"2744088567"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2017.8006584","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2017.8006584","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Information Theory (ISIT)","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/A5035440978","display_name":"Morteza Ashraphijuo","orcid":"https://orcid.org/0000-0001-9324-620X"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Morteza Ashraphijuo","raw_affiliation_strings":["Columbia University"],"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064822688","display_name":"Vaneet Aggarwal","orcid":"https://orcid.org/0000-0001-9131-4723"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vaneet Aggarwal","raw_affiliation_strings":["Purdue University"],"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100382658","display_name":"Xiaodong Wang","orcid":"https://orcid.org/0000-0002-2945-9240"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaodong Wang","raw_affiliation_strings":["Columbia University"],"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5035440978"],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":2.8812,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.90484456,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"531","last_page":"535"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9987999796867371,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.996999979019165,"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/characterization","display_name":"Characterization (materials science)","score":0.5549274682998657},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5384116172790527},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5095906257629395},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5045994520187378},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.49976658821105957},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31128066778182983},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.16176030039787292},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.0634428858757019}],"concepts":[{"id":"https://openalex.org/C2780841128","wikidata":"https://www.wikidata.org/wiki/Q5073781","display_name":"Characterization (materials science)","level":2,"score":0.5549274682998657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5384116172790527},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5095906257629395},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5045994520187378},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.49976658821105957},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31128066778182983},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.16176030039787292},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0634428858757019},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit.2017.8006584","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2017.8006584","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Information Theory (ISIT)","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":34,"referenced_works":["https://openalex.org/W217710249","https://openalex.org/W1580960713","https://openalex.org/W1621556183","https://openalex.org/W2029437570","https://openalex.org/W2030628896","https://openalex.org/W2048050794","https://openalex.org/W2078677240","https://openalex.org/W2081962379","https://openalex.org/W2102019642","https://openalex.org/W2103972604","https://openalex.org/W2106005123","https://openalex.org/W2106295515","https://openalex.org/W2109240917","https://openalex.org/W2134332047","https://openalex.org/W2136559714","https://openalex.org/W2290646310","https://openalex.org/W2364072717","https://openalex.org/W2404322182","https://openalex.org/W2471400908","https://openalex.org/W2517389697","https://openalex.org/W2570015289","https://openalex.org/W2583357717","https://openalex.org/W2585471473","https://openalex.org/W2587931976","https://openalex.org/W2600627466","https://openalex.org/W2611328865","https://openalex.org/W2742886838","https://openalex.org/W2963050208","https://openalex.org/W2963476323","https://openalex.org/W6657829019","https://openalex.org/W6707261557","https://openalex.org/W6733231933","https://openalex.org/W6735117213","https://openalex.org/W6736425207"],"related_works":["https://openalex.org/W2781510240","https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W2897298721","https://openalex.org/W4290987221","https://openalex.org/W2216309014","https://openalex.org/W3199841771","https://openalex.org/W3006184558"],"abstract_inverted_index":{"In":[0,34],"this":[1,38,79],"paper,":[2],"we":[3,40,81],"characterize":[4],"the":[5,9,12,47,59,65,70,75,88,99],"deterministic":[6],"conditions":[7],"on":[8,46,69,87],"locations":[10],"of":[11,24,30],"sampled":[13,100],"entries,":[14],"which":[15,50],"are":[16],"equivalent":[17],"(necessary":[18],"and":[19],"sufficient)":[20],"to":[21,36,53],"finite":[22],"completability":[23],"a":[25,84],"tensor":[26,101],"given":[27],"some":[28],"components":[29,57],"its":[31],"Tucker":[32,48],"rank.":[33],"order":[35],"derive":[37,83],"characterization,":[39],"propose":[41],"an":[42],"algebraic":[43],"geometric":[44,67],"analysis":[45,61],"manifold,":[49],"allows":[51],"us":[52],"incorporate":[54],"multiple":[55],"rank":[56],"in":[58,62],"proposed":[60],"contrast":[63],"with":[64],"conventional":[66],"approaches":[68],"Grassmannian":[71],"manifold.":[72],"Then,":[73],"using":[74],"developed":[76],"tools":[77],"for":[78,98],"analysis,":[80],"also":[82],"sufficient":[85],"condition":[86],"sampling":[89],"pattern":[90],"that":[91],"ensures":[92],"there":[93],"exists":[94],"only":[95],"one":[96],"completion":[97],"(unique":[102],"completability).":[103]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
