{"id":"https://openalex.org/W4387587617","doi":"https://doi.org/10.1109/tsp.2023.3322813","title":"Quantized Low-Rank Multivariate Regression With Random Dithering","display_name":"Quantized Low-Rank Multivariate Regression With Random Dithering","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387587617","doi":"https://doi.org/10.1109/tsp.2023.3322813"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2023.3322813","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tsp.2023.3322813","pdf_url":"https://ieeexplore.ieee.org/ielx7/78/4359509/10283471.pdf","source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/78/4359509/10283471.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101540000","display_name":"Junren Chen","orcid":"https://orcid.org/0000-0003-3606-9598"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Junren Chen","raw_affiliation_strings":["Department of Mathematics, The University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0003-3606-9598","affiliations":[{"raw_affiliation_string":"Department of Mathematics, The University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100735550","display_name":"Yueqi Wang","orcid":"https://orcid.org/0009-0008-0579-8468"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yueqi Wang","raw_affiliation_strings":["Department of Mathematics, The University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0009-0008-0579-8468","affiliations":[{"raw_affiliation_string":"Department of Mathematics, The University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010561682","display_name":"Michael K. Ng","orcid":"https://orcid.org/0000-0001-6833-5227"},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Michael K. Ng","raw_affiliation_strings":["Department of Mathematics, Hong Kong Baptist University, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-6833-5227","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Hong Kong Baptist University, Hong Kong, China","institution_ids":["https://openalex.org/I141568987"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4689,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.79093675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"71","issue":null,"first_page":"3913","last_page":"3928"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/dither","display_name":"Dither","score":0.7615817785263062},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6499173641204834},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6339889168739319},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5536473989486694},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.5233199596405029},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4883003234863281},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.44788026809692383},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4370739161968231},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.36395567655563354},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.30352067947387695},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2781721353530884},{"id":"https://openalex.org/keywords/noise-shaping","display_name":"Noise shaping","score":0.12552973628044128}],"concepts":[{"id":"https://openalex.org/C70451592","wikidata":"https://www.wikidata.org/wiki/Q376493","display_name":"Dither","level":3,"score":0.7615817785263062},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6499173641204834},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6339889168739319},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5536473989486694},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.5233199596405029},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4883003234863281},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.44788026809692383},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4370739161968231},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.36395567655563354},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.30352067947387695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2781721353530884},{"id":"https://openalex.org/C9083635","wikidata":"https://www.wikidata.org/wiki/Q2133535","display_name":"Noise shaping","level":2,"score":0.12552973628044128},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2023.3322813","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tsp.2023.3322813","pdf_url":"https://ieeexplore.ieee.org/ielx7/78/4359509/10283471.pdf","source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/tsp.2023.3322813","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tsp.2023.3322813","pdf_url":"https://ieeexplore.ieee.org/ielx7/78/4359509/10283471.pdf","source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387587617.pdf","grobid_xml":"https://content.openalex.org/works/W4387587617.grobid-xml"},"referenced_works_count":87,"referenced_works":["https://openalex.org/W1627054999","https://openalex.org/W1969976272","https://openalex.org/W1987653354","https://openalex.org/W1991266025","https://openalex.org/W2028859642","https://openalex.org/W2040176268","https://openalex.org/W2048181291","https://openalex.org/W2057122525","https://openalex.org/W2060430274","https://openalex.org/W2065180801","https://openalex.org/W2081297271","https://openalex.org/W2084745075","https://openalex.org/W2100403032","https://openalex.org/W2101593431","https://openalex.org/W2105710559","https://openalex.org/W2111297856","https://openalex.org/W2112895071","https://openalex.org/W2116950495","https://openalex.org/W2121716262","https://openalex.org/W2135046866","https://openalex.org/W2141907517","https://openalex.org/W2151007095","https://openalex.org/W2161920970","https://openalex.org/W2162451874","https://openalex.org/W2293423476","https://openalex.org/W2398269536","https://openalex.org/W2469490737","https://openalex.org/W2524428287","https://openalex.org/W2526125075","https://openalex.org/W2535838896","https://openalex.org/W2586353914","https://openalex.org/W2616050959","https://openalex.org/W2741269719","https://openalex.org/W2787248994","https://openalex.org/W2910864390","https://openalex.org/W2913340405","https://openalex.org/W2938943746","https://openalex.org/W2950190315","https://openalex.org/W2962769133","https://openalex.org/W2962910706","https://openalex.org/W2963122961","https://openalex.org/W2963158056","https://openalex.org/W2963826549","https://openalex.org/W2963924474","https://openalex.org/W2963973365","https://openalex.org/W2964200481","https://openalex.org/W2964322027","https://openalex.org/W2968828697","https://openalex.org/W2974585710","https://openalex.org/W2975043678","https://openalex.org/W2979011527","https://openalex.org/W2988817072","https://openalex.org/W3000691386","https://openalex.org/W3015757129","https://openalex.org/W3033664100","https://openalex.org/W3042484720","https://openalex.org/W3083751958","https://openalex.org/W3102567422","https://openalex.org/W3103870504","https://openalex.org/W3104447032","https://openalex.org/W3148730259","https://openalex.org/W3165260969","https://openalex.org/W3189021302","https://openalex.org/W3192637965","https://openalex.org/W3197795948","https://openalex.org/W4205211994","https://openalex.org/W4221143621","https://openalex.org/W4254197176","https://openalex.org/W4285166024","https://openalex.org/W4288366593","https://openalex.org/W4288612157","https://openalex.org/W4292363360","https://openalex.org/W4297687186","https://openalex.org/W4365130676","https://openalex.org/W4388145434","https://openalex.org/W6676665419","https://openalex.org/W6677837081","https://openalex.org/W6680344438","https://openalex.org/W6712661769","https://openalex.org/W6720242923","https://openalex.org/W6727208969","https://openalex.org/W6727690081","https://openalex.org/W6741986022","https://openalex.org/W6758557334","https://openalex.org/W6768511045","https://openalex.org/W6799349547","https://openalex.org/W6850062474"],"related_works":["https://openalex.org/W2389128124","https://openalex.org/W2364812720","https://openalex.org/W2138725603","https://openalex.org/W3001913892","https://openalex.org/W2050335563","https://openalex.org/W2273639645","https://openalex.org/W4210717739","https://openalex.org/W2000868246","https://openalex.org/W2933484786","https://openalex.org/W2067936044"],"abstract_inverted_index":{"Low-rank":[0],"multivariate":[1],"regression":[2,18,151],"(LRMR)":[3],"is":[4],"an":[5],"important":[6],"statistical":[7],"learning":[8],"model":[9,152],"that":[10,62],"combines":[11],"highly":[12],"correlated":[13],"tasks":[14],"as":[15,170,172],"a":[16,34,149,173],"multiresponse":[17],"problem":[19],"with":[20,73,153],"low-rank":[21,150],"priori":[22],"on":[23,50,101,165],"the":[24,38,41,51,54,83,102,107,116,121,125,136,140],"coefficient":[25,56],"matrix.":[26,57],"In":[27],"this":[28],"paper,":[29],"we":[30,69,77,105,144],"study":[31],"quantized":[32,103],"LRMR,":[33],"practical":[35],"setting":[36],"where":[37],"responses":[39,96],"and/or":[40],"covariates":[42],"are":[43,93],"discretized":[44],"to":[45,82,148],"finite":[46],"precision.":[47],"We":[48,156],"focus":[49],"estimation":[52],"of":[53,123],"underlying":[55],"To":[58],"make":[59],"consistent":[60],"estimator":[61],"could":[63],"achieve":[64,127],"arbitrarily":[65],"small":[66],"error":[67,118,141],"possible,":[68],"employ":[70],"uniform":[71,88],"quantization":[72,132],"random":[74,80],"dithering,":[75,124],"i.e.,":[76],"add":[78],"appropriate":[79],"noise":[81],"data":[84,175],"before":[85],"quantization.":[86],"Specifically,":[87],"dither":[89,92],"and":[90,97,110,114,158],"triangular":[91],"used":[94],"for":[95],"covariates,":[98],"respectively.":[99],"Based":[100],"data,":[104,167],"propose":[106],"constrained":[108],"Lasso":[109,112],"regularized":[111],"estimators,":[113],"derive":[115],"non-asymptotic":[117],"bounds.":[119],"With":[120],"aid":[122],"estimators":[126],"minimax":[128],"optimal":[129],"rate,":[130],"while":[131],"only":[133],"slightly":[134],"worsens":[135],"multiplicative":[137],"factor":[138],"in":[139],"rate.":[142],"Moreover,":[143],"extend":[145],"our":[146,160],"results":[147,162],"matrix":[154],"responses.":[155],"corroborate":[157],"demonstrate":[159],"theoretical":[161],"via":[163],"simulations":[164],"synthetic":[166],"image":[168],"restoration,":[169],"well":[171],"real":[174],"application.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
