{"id":"https://openalex.org/W4388117345","doi":"https://doi.org/10.23919/eusipco58844.2023.10290064","title":"Estimating Joint Probability Distribution with Low-Rank Tensor Decomposition, Radon Transforms and Dictionaries","display_name":"Estimating Joint Probability Distribution with Low-Rank Tensor Decomposition, Radon Transforms and Dictionaries","publication_year":2023,"publication_date":"2023-09-04","ids":{"openalex":"https://openalex.org/W4388117345","doi":"https://doi.org/10.23919/eusipco58844.2023.10290064"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco58844.2023.10290064","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/eusipco58844.2023.10290064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 31st European Signal Processing Conference (EUSIPCO)","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/A5017813499","display_name":"Pranava Singhal","orcid":"https://orcid.org/0009-0004-0166-1529"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Pranava Singhal","raw_affiliation_strings":["IIT Bombay,Department of EE,Mumbai,India","Department of EE, IIT Bombay, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"IIT Bombay,Department of EE,Mumbai,India","institution_ids":["https://openalex.org/I162827531"]},{"raw_affiliation_string":"Department of EE, IIT Bombay, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088203313","display_name":"Waqar Mirza","orcid":null},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Waqar Mirza","raw_affiliation_strings":["IIT Bombay,Department of EE,Mumbai,India","Department of EE, IIT Bombay, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"IIT Bombay,Department of EE,Mumbai,India","institution_ids":["https://openalex.org/I162827531"]},{"raw_affiliation_string":"Department of EE, IIT Bombay, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072824358","display_name":"Ajit Rajwade","orcid":"https://orcid.org/0000-0001-6463-3315"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ajit Rajwade","raw_affiliation_strings":["IIT Bombay,Department of CSE,Mumbai,India","Department of CSE, IIT Bombay, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"IIT Bombay,Department of CSE,Mumbai,India","institution_ids":["https://openalex.org/I162827531"]},{"raw_affiliation_string":"Department of CSE, IIT Bombay, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063624532","display_name":"Karthik S. Gurumoorthy","orcid":"https://orcid.org/0000-0002-2483-3723"},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karthik S. Gurumoorthy","raw_affiliation_strings":["U.S. Omni Tech, Walmart Global Tech,Bangalore,India","U.S. Omni Tech, Walmart Global Tech, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"U.S. Omni Tech, Walmart Global Tech,Bangalore,India","institution_ids":["https://openalex.org/I1330693074"]},{"raw_affiliation_string":"U.S. Omni Tech, Walmart Global Tech, Bangalore, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5017813499"],"corresponding_institution_ids":["https://openalex.org/I162827531"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13405797,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1988","last_page":"1992"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9869999885559082,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9832000136375427,"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/joint-probability-distribution","display_name":"Joint probability distribution","score":0.691929280757904},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6414691209793091},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.5076271295547485},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5024101734161377},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49940943717956543},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.492719441652298},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.47987237572669983},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45479729771614075},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.4283599853515625},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4263582229614258},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41828081011772156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33862945437431335},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3327629566192627},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3240254521369934},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.12028813362121582}],"concepts":[{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.691929280757904},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6414691209793091},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.5076271295547485},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5024101734161377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49940943717956543},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.492719441652298},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.47987237572669983},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45479729771614075},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.4283599853515625},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4263582229614258},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41828081011772156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33862945437431335},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3327629566192627},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3240254521369934},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.12028813362121582},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco58844.2023.10290064","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/eusipco58844.2023.10290064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 31st European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W2125118959","https://openalex.org/W2752365628","https://openalex.org/W2774921216","https://openalex.org/W2920809662","https://openalex.org/W3038413610","https://openalex.org/W3104256134","https://openalex.org/W3125149544","https://openalex.org/W3193612066","https://openalex.org/W4287684677","https://openalex.org/W4312259433","https://openalex.org/W6760029585","https://openalex.org/W6795818240"],"related_works":["https://openalex.org/W1987264987","https://openalex.org/W2951801950","https://openalex.org/W4297670780","https://openalex.org/W2893341095","https://openalex.org/W1482189126","https://openalex.org/W871299571","https://openalex.org/W4241043257","https://openalex.org/W2122857041","https://openalex.org/W2102345963","https://openalex.org/W3130165856"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"describe":[4],"a":[5,26,40],"method":[6,112],"for":[7,104],"estimating":[8,114],"the":[9,19,44,57,77,96,108,122,139],"joint":[10,45,78],"probability":[11,116],"density":[12,46],"from":[13,47,80,91],"data":[14],"samples":[15],"by":[16,100],"assuming":[17],"that":[18],"underlying":[20],"distribution":[21,79],"can":[22,51],"be":[23,52],"decomposed":[24],"as":[25],"mixture":[27,33],"of":[28,60,110],"product":[29],"densities":[30,117],"with":[31,56,121],"few":[32],"components.":[34],"Prior":[35],"works":[36],"have":[37],"used":[38],"such":[39],"decomposition":[41],"to":[42,68,75],"estimate":[43,76],"lower-dimensional":[48],"marginals,":[49,82],"which":[50],"estimated":[53],"more":[54],"reliably":[55],"same":[58],"number":[59],"samples.":[61],"We":[62,106],"combine":[63],"two":[64],"key":[65],"ideas:":[66],"dictionaries":[67],"represent":[69],"1-D":[70,81,102],"densities,":[71],"and":[72,118,126],"random":[73],"projections":[74],"explored":[83],"separately":[84],"in":[85,137],"prior":[86],"work.":[87],"Our":[88,131],"algorithm":[89,132],"benefits":[90],"improved":[92],"sample":[93],"complexity":[94],"over":[95],"previous":[97,123],"dictionary-based":[98,124],"approach":[99,125],"using":[101],"marginals":[103],"reconstruction.":[105],"evaluate":[107],"performance":[109],"our":[111],"on":[113],"synthetic":[115],"compare":[119],"it":[120],"Gaussian":[127],"Mixture":[128],"Models":[129],"(GMMs).":[130],"outperforms":[133],"these":[134],"other":[135],"approaches":[136],"all":[138],"experimental":[140],"settings.":[141]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
