{"id":"https://openalex.org/W3160709304","doi":"https://doi.org/10.1109/icpr48806.2021.9412245","title":"Directionally Paired Principal Component Analysis for Bivariate Estimation Problems","display_name":"Directionally Paired Principal Component Analysis for Bivariate Estimation Problems","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3160709304","doi":"https://doi.org/10.1109/icpr48806.2021.9412245","mag":"3160709304","pmid":"https://pubmed.ncbi.nlm.nih.gov/34337618"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8323711","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017360469","display_name":"Yifei Fan","orcid":"https://orcid.org/0000-0002-4762-1010"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yifei Fan","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA","School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA","institution_ids":["https://openalex.org/I130701444"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070143939","display_name":"Navdeep Dahiya","orcid":"https://orcid.org/0000-0002-6275-6845"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Navdeep Dahiya","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA","School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA","institution_ids":["https://openalex.org/I130701444"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016583911","display_name":"Samuel Bignardi","orcid":"https://orcid.org/0000-0002-5970-6265"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Samuel Bignardi","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA","School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA","institution_ids":["https://openalex.org/I130701444"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070821128","display_name":"Romeil Sandhu","orcid":"https://orcid.org/0000-0002-7659-7590"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Romeil Sandhu","raw_affiliation_strings":["Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA","Stony Brook University, Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014124403","display_name":"Anthony Yezzi","orcid":"https://orcid.org/0000-0002-3771-4889"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anthony Yezzi","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA","School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA","institution_ids":["https://openalex.org/I130701444"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5017360469"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.118,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.37349808,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"10180","last_page":"10187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9879000186920166,"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/unobservable","display_name":"Unobservable","score":0.8633191585540771},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.8314870595932007},{"id":"https://openalex.org/keywords/observable","display_name":"Observable","score":0.7062909007072449},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5962769985198975},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.5663076043128967},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.5641428232192993},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5578128099441528},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5355835556983948},{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.5328909158706665},{"id":"https://openalex.org/keywords/principal-component-regression","display_name":"Principal component regression","score":0.5122625231742859},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4937630593776703},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.481442391872406},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.478935569524765},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4605047404766083},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.42089247703552246},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3978511393070221},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38335585594177246},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.36130160093307495},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35899341106414795},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34084269404411316},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1457083821296692}],"concepts":[{"id":"https://openalex.org/C2780695315","wikidata":"https://www.wikidata.org/wiki/Q3799040","display_name":"Unobservable","level":2,"score":0.8633191585540771},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8314870595932007},{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.7062909007072449},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5962769985198975},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.5663076043128967},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.5641428232192993},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5578128099441528},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5355835556983948},{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.5328909158706665},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.5122625231742859},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4937630593776703},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.481442391872406},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.478935569524765},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4605047404766083},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.42089247703552246},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3978511393070221},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38335585594177246},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36130160093307495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35899341106414795},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34084269404411316},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1457083821296692},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmid:34337618","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34337618","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:8323711","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8323711","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IAPR Int Conf Pattern Recogn","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:8323711","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8323711","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IAPR Int Conf Pattern Recogn","raw_type":"Text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3683667027","display_name":null,"funder_award_id":"W911NF-18-1-0281","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G6573961238","display_name":null,"funder_award_id":"R01 HL143350","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6678150830","display_name":null,"funder_award_id":"ECCS-1749937","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W55399194","https://openalex.org/W1493968464","https://openalex.org/W2025341678","https://openalex.org/W2100039146","https://openalex.org/W2105942117","https://openalex.org/W2122538988","https://openalex.org/W2137107481","https://openalex.org/W2174400750","https://openalex.org/W2283039974","https://openalex.org/W2294798173","https://openalex.org/W2341171179","https://openalex.org/W3008214279","https://openalex.org/W3124484778","https://openalex.org/W4237591687","https://openalex.org/W4237723258","https://openalex.org/W6602219098","https://openalex.org/W6680483772"],"related_works":["https://openalex.org/W1624628591","https://openalex.org/W2017698186","https://openalex.org/W2022816325","https://openalex.org/W2468593193","https://openalex.org/W3123801500","https://openalex.org/W2981666789","https://openalex.org/W2058478026","https://openalex.org/W1966115210","https://openalex.org/W2042294628","https://openalex.org/W4396824786"],"abstract_inverted_index":{"We":[0,62],"propose":[1],"Directionally":[2],"Paired":[3],"Principal":[4],"Component":[5],"Analysis":[6],"(DP-PCA),":[7],"a":[8,93,102,127,161],"novel":[9],"linear":[10,76],"dimension-reduction":[11],"model":[12],"for":[13,55,148,166,173],"estimating":[14],"coupled":[15],"yet":[16],"partially":[17],"observable":[18,57,119,150,168],"variable":[19,124],"sets.":[20],"Unlike":[21],"partial":[22,27],"least":[23,28],"squares":[24,29],"methods":[25,78],"(e.g.,":[26],"regression":[30,90],"and":[31,52,58,73,82,92,121,170],"canonical":[32],"correlation":[33],"analysis)":[34],"that":[35,99],"maximize":[36],"correlation/covariance":[37],"between":[38],"the":[39,50,56,64,67,109,113,118,122,130,134,139,149,167,174],"two":[40],"datasets,":[41,91],"our":[42],"DP-PCA":[43,69,111,132,172],"directly":[44],"minimizes,":[45],"either":[46],"conditionally":[47],"or":[48],"unconditionally,":[49],"reconstruction":[51,81,115],"prediction":[53,83,136],"errors":[54,137],"unobservable":[59,140,175],"part,":[60],"respectively.":[61],"demonstrate":[63],"optimality":[65],"of":[66,105],"proposed":[68],"approach,":[70],"we":[71],"compare":[72],"evaluate":[74],"relevant":[75],"cross-decomposition":[77],"with":[79],"data":[80],"experiments":[84],"on":[85,117,138],"synthetic":[86],"Gaussian":[87],"data,":[88],"multi-target":[89],"single-channel":[94],"image":[95],"dataset.":[96],"Results":[97],"show":[98],"when":[100],"only":[101],"single":[103],"pair":[104],"bases":[106],"is":[107,146],"allowed,":[108],"conditional":[110],"achieves":[112],"lowest":[114,135],"error":[116],"part":[120,169],"total":[123],"sets":[125],"as":[126],"whole;":[128],"meanwhile,":[129],"unconditional":[131,171],"reaches":[133],"part.":[141,176],"When":[142],"an":[143,157],"extra":[144],"budget":[145],"allowed":[147],"part's":[151],"PCA":[152,165],"basis,":[153],"one":[154],"can":[155],"reach":[156],"optimal":[158],"solution":[159],"using":[160],"combined":[162],"method:":[163],"standard":[164]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
