{"id":"https://openalex.org/W2767298482","doi":"https://doi.org/10.1109/icdsp.2017.8096131","title":"Brain image completion by Bayesian tensor decomposition","display_name":"Brain image completion by Bayesian tensor decomposition","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2767298482","doi":"https://doi.org/10.1109/icdsp.2017.8096131","mag":"2767298482"},"language":"en","primary_location":{"id":"doi:10.1109/icdsp.2017.8096131","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2017.8096131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Digital Signal Processing (DSP)","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/A5071624109","display_name":"Lihua Gui","orcid":null},"institutions":[{"id":"https://openalex.org/I185365093","display_name":"Saitama Institute of Technology","ror":"https://ror.org/01pkeax38","country_code":"JP","type":"education","lineage":["https://openalex.org/I185365093"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Lihua Gui","raw_affiliation_strings":["Graduate School of Engineering, Saitama Institute of Technology, Fukaya, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Saitama Institute of Technology, Fukaya, Japan","institution_ids":["https://openalex.org/I185365093"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083182987","display_name":"Qibin Zhao","orcid":"https://orcid.org/0000-0002-4442-3182"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qibin Zhao","raw_affiliation_strings":["Tensor Learning Unit RIKEN AIP, Guangdong University of Technology"],"affiliations":[{"raw_affiliation_string":"Tensor Learning Unit RIKEN AIP, Guangdong University of Technology","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101714954","display_name":"Jianting Cao","orcid":"https://orcid.org/0000-0002-7749-7188"},"institutions":[{"id":"https://openalex.org/I185365093","display_name":"Saitama Institute of Technology","ror":"https://ror.org/01pkeax38","country_code":"JP","type":"education","lineage":["https://openalex.org/I185365093"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jianting Cao","raw_affiliation_strings":["Graduate School of Engineering, Saitama Institute of Technology, Fukaya, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Saitama Institute of Technology, Fukaya, Japan","institution_ids":["https://openalex.org/I185365093"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071624109"],"corresponding_institution_ids":["https://openalex.org/I185365093"],"apc_list":null,"apc_paid":null,"fwci":0.1153,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.40127389,"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":"1","last_page":"4"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9976999759674072,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.995199978351593,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multilinear-map","display_name":"Multilinear map","score":0.8091291785240173},{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.7898417115211487},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6436237692832947},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.6355741620063782},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6276278495788574},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6118026375770569},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6065305471420288},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5755202174186707},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5567302703857422},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48571524024009705},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4594901204109192},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4377056658267975},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.41643065214157104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.314139723777771},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.2663174271583557}],"concepts":[{"id":"https://openalex.org/C84392682","wikidata":"https://www.wikidata.org/wiki/Q1952404","display_name":"Multilinear map","level":2,"score":0.8091291785240173},{"id":"https://openalex.org/C42704193","wikidata":"https://www.wikidata.org/wiki/Q7851097","display_name":"Tucker decomposition","level":4,"score":0.7898417115211487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6436237692832947},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6355741620063782},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6276278495788574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6118026375770569},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6065305471420288},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5755202174186707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5567302703857422},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48571524024009705},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4594901204109192},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4377056658267975},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.41643065214157104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.314139723777771},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.2663174271583557},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdsp.2017.8096131","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2017.8096131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Digital Signal Processing (DSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W792141054","https://openalex.org/W2024165284","https://openalex.org/W2025828896","https://openalex.org/W2033560557","https://openalex.org/W2091449379","https://openalex.org/W2098765040","https://openalex.org/W2147512299","https://openalex.org/W2149403928","https://openalex.org/W2160047866","https://openalex.org/W2395623988","https://openalex.org/W6681543329"],"related_works":["https://openalex.org/W4318719034","https://openalex.org/W37958683","https://openalex.org/W1999178348","https://openalex.org/W2535617683","https://openalex.org/W2164129707","https://openalex.org/W4292122269","https://openalex.org/W2949366006","https://openalex.org/W4226317016","https://openalex.org/W2922481674","https://openalex.org/W2464767573"],"abstract_inverted_index":{"The":[0,10],"MRI":[1,13,48,91,109],"data":[2,28,49],"is":[3,18],"naturally":[4],"represented":[5],"by":[6],"a":[7,19],"three-order":[8],"tensor.":[9],"reconstruction":[11],"of":[12,47],"image":[14,92,110],"from":[15,50],"sparse":[16],"observations":[17],"challenging":[20],"task,":[21],"which":[22,76],"has":[23],"many":[24],"potential":[25],"applications":[26],"for":[27],"compression,":[29],"feature":[30],"extraction":[31],"and":[32,62],"classifications.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37],"employ":[38],"Bayesian":[39,74],"Tucker":[40],"decomposition":[41],"to":[42,90],"learn":[43],"the":[44,56,65,79,87,97,107],"low-rank":[45],"representations":[46],"partially":[51],"observed":[52],"voxels.":[53],"By":[54],"specifying":[55],"sparsity":[57],"priors":[58],"over":[59],"factor":[60],"matrices":[61],"core":[63],"tensor,":[64],"multilinear":[66],"ranks":[67],"can":[68,104],"be":[69],"automatically":[70],"determined":[71],"via":[72],"variational":[73],"inference,":[75],"thus":[77],"avoids":[78],"difficulty":[80],"in":[81],"tuning":[82],"parameters":[83],"empirically.":[84],"We":[85],"apply":[86],"proposed":[88],"method":[89,103],"with":[93,111],"50-80%":[94],"missing":[95],"voxels,":[96],"experimental":[98],"results":[99],"demonstrate":[100],"that":[101],"our":[102],"effectively":[105],"recover":[106],"whole":[108],"high":[112],"predictive":[113],"performance.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
