{"id":"https://openalex.org/W7161976362","doi":"https://doi.org/10.48550/arxiv.2605.20559","title":"Group-Aware Matrix Estimation and Latent Subspace Recovery","display_name":"Group-Aware Matrix Estimation and Latent Subspace Recovery","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7161976362","doi":"https://doi.org/10.48550/arxiv.2605.20559"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20559","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20559","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.20559","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136632465","display_name":"Hamza Golubovic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golubovic, Hamza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136651857","display_name":"Matthew Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Matthew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048412925","display_name":"Genevera I. Allen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Allen, Genevera I.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002283094","display_name":"Tarek M. Zikry","orcid":"https://orcid.org/0000-0003-3360-249X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zikry, Tarek M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.46320000290870667,"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":0.46320000290870667,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.12690000236034393,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1216999962925911,"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/subspace-topology","display_name":"Subspace topology","score":0.7418000102043152},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6312999725341797},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5997999906539917},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.5733000040054321},{"id":"https://openalex.org/keywords/block-matrix","display_name":"Block matrix","score":0.5730000138282776},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5299999713897705},{"id":"https://openalex.org/keywords/row","display_name":"Row","score":0.5045999884605408},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47870001196861267},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4722000062465668}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7418000102043152},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6312999725341797},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5997999906539917},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.5733000040054321},{"id":"https://openalex.org/C85817219","wikidata":"https://www.wikidata.org/wiki/Q884772","display_name":"Block matrix","level":3,"score":0.5730000138282776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5547000169754028},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5299999713897705},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47870001196861267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47760000824928284},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.42080000042915344},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4147000014781952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41280001401901245},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.41130000352859497},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C104140500","wikidata":"https://www.wikidata.org/wiki/Q2088159","display_name":"Row and column spaces","level":3,"score":0.32089999318122864},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.31150001287460327},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.2815999984741211},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27799999713897705},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27649998664855957},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2551000118255615},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C2780985081","wikidata":"https://www.wikidata.org/wiki/Q676502","display_name":"Data Matrix","level":5,"score":0.2522999942302704},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20559","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20559","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.20559","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20559","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"matrix":[1,75],"completion":[2],"problems":[3],"often":[4],"involve":[5],"heterogeneous":[6],"data":[7],"whose":[8],"rows":[9],"simultaneously":[10],"belong":[11],"to":[12,88],"many":[13],"meta-categories,":[14],"such":[15],"as":[16],"demographic":[17],"and":[18,26,109,122,130,150,166],"age":[19],"groups":[20,87],"in":[21,30,96,139],"recommendation":[22],"systems,":[23],"or":[24,160],"region":[25],"recording":[27],"session":[28],"labels":[29],"neural":[31],"electrophysiological":[32],"experiments.":[33],"Standard":[34],"low-rank":[35,74,178],"estimators":[36],"impose":[37],"a":[38,68,97],"single":[39],"global":[40,163],"latent":[41,94,151],"geometry,":[42],"which":[43],"can":[44],"recover":[45],"average":[46],"structure":[47,95],"but":[48],"may":[49],"smooth":[50],"away":[51],"subgroup-specific":[52,110],"variation,":[53],"especially":[54],"when":[55,174],"observations":[56],"are":[57],"unevenly":[58],"distributed":[59],"across":[60],"groups.":[61],"We":[62,101],"introduce":[63],"Group-Aware":[64],"Matrix":[65],"Estimation":[66],"(GAME),":[67],"convex":[69],"estimator":[70],"for":[71,105],"overlapping":[72,82],"subgroup-wise":[73],"estimation.":[76],"GAME":[77,135,157],"regularizes":[78],"category-specific":[79],"submatrices":[80],"through":[81],"nuclear-norm":[83],"penalties,":[84],"allowing":[85],"related":[86],"borrow":[89],"information":[90],"while":[91],"preserving":[92],"local":[93],"shared":[98],"coordinate":[99],"system.":[100],"provide":[102],"finite-sample":[103],"guarantees":[104],"both":[106,147],"reconstruction":[107,148],"error":[108],"subspace":[111,152],"recovery,":[112],"showing":[113],"how":[114],"performance":[115],"depends":[116],"on":[117,126],"sampling":[118],"density,":[119],"subgroup":[120],"rank,":[121],"overlap":[123],"structure.":[124,179],"Experiments":[125],"synthetic,":[127],"recommendation,":[128],"ecological,":[129],"neuroscience":[131],"datasets":[132],"show":[133],"that":[134],"is":[136,158],"most":[137],"beneficial":[138],"structured":[140],"missingness":[141],"regimes,":[142],"where":[143],"subgroup-aware":[144],"regularization":[145],"improves":[146],"accuracy":[149],"fidelity.":[153],"Across":[154],"these":[155],"benchmarks,":[156],"competitive":[159],"best":[161],"among":[162],"low-rank,":[164],"side-information,":[165],"modern":[167],"imputation":[168],"baselines,":[169],"with":[170],"the":[171],"largest":[172],"gains":[173],"subgroups":[175],"exhibit":[176],"distinct":[177]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-22T00:00:00"}