{"id":"https://openalex.org/W2964304544","doi":"https://doi.org/10.1109/icassp.2012.6288341","title":"LOw-rank data modeling via the minimum description length principle","display_name":"LOw-rank data modeling via the minimum description length principle","publication_year":2012,"publication_date":"2012-03-01","ids":{"openalex":"https://openalex.org/W2964304544","doi":"https://doi.org/10.1109/icassp.2012.6288341","mag":"2964304544"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2012.6288341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2012.6288341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5102842247","display_name":"Ignacio Ram\u00edrez","orcid":"https://orcid.org/0000-0003-2954-9040"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ignacio Ramirez","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025218580","display_name":"Guillermo Sapiro","orcid":"https://orcid.org/0000-0001-9190-6964"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guillermo Sapiro","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102842247"],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":1.0286,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78610118,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2165","last_page":"2168"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9958999752998352,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9934999942779541,"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/minimum-description-length","display_name":"Minimum description length","score":0.7382607460021973},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.7353342175483704},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6834279298782349},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5808368921279907},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5772809982299805},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5363268852233887},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"Low-rank approximation","score":0.49139514565467834},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45857885479927063},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.44763949513435364},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.42040228843688965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3885294497013092},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3542305827140808},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32797345519065857},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22514116764068604}],"concepts":[{"id":"https://openalex.org/C87465248","wikidata":"https://www.wikidata.org/wiki/Q1417790","display_name":"Minimum description length","level":2,"score":0.7382607460021973},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.7353342175483704},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6834279298782349},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5808368921279907},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5772809982299805},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5363268852233887},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.49139514565467834},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45857885479927063},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.44763949513435364},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.42040228843688965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3885294497013092},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3542305827140808},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32797345519065857},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22514116764068604},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"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/C25023664","wikidata":"https://www.wikidata.org/wiki/Q1575637","display_name":"Hankel matrix","level":2,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp.2012.6288341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2012.6288341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.767.4834","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.767.4834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1109.6297.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1492459858","https://openalex.org/W1554944419","https://openalex.org/W1736339626","https://openalex.org/W1966096622","https://openalex.org/W2034323860","https://openalex.org/W2054658115","https://openalex.org/W2099111195","https://openalex.org/W2102098892","https://openalex.org/W2131628350","https://openalex.org/W2145962650","https://openalex.org/W2171033594","https://openalex.org/W2321124713","https://openalex.org/W2478708596","https://openalex.org/W2573934436","https://openalex.org/W6629389771"],"related_works":["https://openalex.org/W4298831272","https://openalex.org/W2489956408","https://openalex.org/W349256592","https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W3106318770","https://openalex.org/W3144354057","https://openalex.org/W4385556248","https://openalex.org/W3213035342","https://openalex.org/W3213049004"],"abstract_inverted_index":{"Robust":[0],"low-rank":[1,42],"matrix":[2,43,51],"estimation":[3],"is":[4,52,62],"a":[5,14,45,64,74,110,120,124],"topic":[6],"of":[7,16,33,48,70,89,102,136],"increasing":[8],"interest,":[9],"with":[10,140],"promising":[11],"applications":[12],"in":[13,146],"variety":[15],"fields,":[17],"from":[18,79],"computer":[19],"vision":[20],"to":[21,37,115],"data":[22,35,128],"mining":[23],"and":[24],"recommender":[25],"systems.":[26],"Recent":[27],"theoretical":[28],"results":[29,141],"establish":[30],"the":[31,39,49,67,71,87,90,103,127,133],"ability":[32],"such":[34],"models":[36],"recover":[38],"true":[40,68],"underlying":[41],"when":[44],"large":[46],"portion":[47],"measured":[50],"either":[53],"missing":[54],"or":[55],"arbitrarily":[56],"corrupted.":[57],"However,":[58],"if":[59],"low":[60],"rank":[61,88],"not":[63],"hypothesis":[65],"about":[66],"nature":[69],"data,":[72],"but":[73],"device":[75],"for":[76,85,122,126,142],"extracting":[77],"regularity":[78],"it,":[80],"no":[81],"current":[82],"guidelines":[83],"exist":[84],"choosing":[86],"estimated":[91],"matrix.":[92],"In":[93],"this":[94,98],"work":[95],"we":[96],"address":[97],"problem":[99],"by":[100],"means":[101],"Minimum":[104],"Description":[105],"Length":[106],"(MDL)":[107],"principle":[108],"-":[109,118],"well":[111],"established":[112],"information-theoretic":[113],"approach":[114,139],"statistical":[116],"inference":[117],"as":[119],"guideline":[121],"selecting":[123],"model":[125],"at":[129],"hand.":[130],"We":[131],"demonstrate":[132],"practical":[134],"usefulness":[135],"our":[137],"formal":[138],"complex":[143],"background":[144],"extraction":[145],"video":[147],"sequences.":[148]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
