{"id":"https://openalex.org/W2156131550","doi":"https://doi.org/10.1109/isit.2014.6875223","title":"Information-theoretically optimal sparse PCA","display_name":"Information-theoretically optimal sparse PCA","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2156131550","doi":"https://doi.org/10.1109/isit.2014.6875223","mag":"2156131550"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2014.6875223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2014.6875223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Symposium on Information Theory","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1402.2238","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041246437","display_name":"Yash Deshpande","orcid":"https://orcid.org/0000-0003-4721-2492"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]},{"id":"https://openalex.org/I4210137306","display_name":"Stanford Medicine","ror":"https://ror.org/03mtd9a03","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210137306","https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yash Deshpande","raw_affiliation_strings":["Department of Electrical Engineering, Stanford, CA, USA","[Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA]"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Stanford, CA, USA","institution_ids":["https://openalex.org/I4210137306"]},{"raw_affiliation_string":"[Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA]","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011999109","display_name":"Andrea Montanari","orcid":"https://orcid.org/0000-0002-0267-8574"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]},{"id":"https://openalex.org/I4210137306","display_name":"Stanford Medicine","ror":"https://ror.org/03mtd9a03","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210137306","https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrea Montanari","raw_affiliation_strings":["Departments of Electrical Engineering and Statistics, Stanford, CA, USA","Depts. of Electr. Eng. & Stat., Stanford Univ., Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Departments of Electrical Engineering and Statistics, Stanford, CA, USA","institution_ids":["https://openalex.org/I4210137306","https://openalex.org/I97018004"]},{"raw_affiliation_string":"Depts. of Electr. Eng. & Stat., Stanford Univ., Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041246437"],"corresponding_institution_ids":["https://openalex.org/I4210137306","https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":3.5397,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.92370965,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2197","last_page":"2201"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9998000264167786,"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"}},{"id":"https://openalex.org/T11716","display_name":"Random Matrices and Applications","score":0.9955999851226807,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sparse-pca","display_name":"Sparse PCA","score":0.9119347333908081},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.7615665197372437},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6912535429000854},{"id":"https://openalex.org/keywords/wishart-distribution","display_name":"Wishart distribution","score":0.6643327474594116},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6529114842414856},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5988969802856445},{"id":"https://openalex.org/keywords/corollary","display_name":"Corollary","score":0.5976266860961914},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5762413740158081},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5567231774330139},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5324656963348389},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4935191869735718},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4684123396873474},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.440984308719635},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4392213523387909},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.439103901386261},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.4184116721153259},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4178861379623413},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3357910215854645},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1592189371585846},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.13421949744224548},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.07012027502059937}],"concepts":[{"id":"https://openalex.org/C24252448","wikidata":"https://www.wikidata.org/wiki/Q7573786","display_name":"Sparse PCA","level":3,"score":0.9119347333908081},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7615665197372437},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6912535429000854},{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.6643327474594116},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6529114842414856},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5988969802856445},{"id":"https://openalex.org/C2780012671","wikidata":"https://www.wikidata.org/wiki/Q1343870","display_name":"Corollary","level":2,"score":0.5976266860961914},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5762413740158081},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5567231774330139},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5324656963348389},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4935191869735718},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4684123396873474},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.440984308719635},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4392213523387909},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.439103901386261},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.4184116721153259},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4178861379623413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3357910215854645},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1592189371585846},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.13421949744224548},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.07012027502059937},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/isit.2014.6875223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2014.6875223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Symposium on Information Theory","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1402.2238","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1402.2238","pdf_url":"https://arxiv.org/pdf/1402.2238","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2156131550","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1402.2238.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.711.1544","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.711.1544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://web.stanford.edu/%7Eyashd/PAPERS/bayes.pdf","raw_type":"text"},{"id":"doi:10.48550/arxiv.1402.2238","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1402.2238","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1402.2238","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1402.2238","pdf_url":"https://arxiv.org/pdf/1402.2238","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2156131550.pdf","grobid_xml":"https://content.openalex.org/works/W2156131550.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1973919743","https://openalex.org/W1975900269","https://openalex.org/W2040969041","https://openalex.org/W2066459155","https://openalex.org/W2088164510","https://openalex.org/W2097714737","https://openalex.org/W2099111195","https://openalex.org/W2099551908","https://openalex.org/W2106084579","https://openalex.org/W2111616148","https://openalex.org/W2118902885","https://openalex.org/W2125295547","https://openalex.org/W2144629557","https://openalex.org/W2149903352","https://openalex.org/W2157638198","https://openalex.org/W2159998236","https://openalex.org/W2615253071","https://openalex.org/W2949962875","https://openalex.org/W3102974460","https://openalex.org/W6676520490","https://openalex.org/W6744841427"],"related_works":["https://openalex.org/W3103647971","https://openalex.org/W3098848552","https://openalex.org/W2332917886","https://openalex.org/W2082029531","https://openalex.org/W1922029667","https://openalex.org/W2066459155","https://openalex.org/W3102974460","https://openalex.org/W1983383403","https://openalex.org/W1520752838","https://openalex.org/W2963072996","https://openalex.org/W2111616148","https://openalex.org/W2040969041","https://openalex.org/W2964213728","https://openalex.org/W2522434885","https://openalex.org/W2097714737","https://openalex.org/W2006452405","https://openalex.org/W2963258773","https://openalex.org/W37129894","https://openalex.org/W2304154586","https://openalex.org/W58673994"],"abstract_inverted_index":{"Sparse":[0],"Principal":[1],"Component":[2],"Analysis":[3],"(PCA)":[4],"is":[5,89],"a":[6,13,17,36,98,106],"dimensionality":[7],"reduction":[8],"technique":[9],"wherein":[10],"one":[11],"seeks":[12],"low-rank":[14],"representation":[15],"of":[16,33,84,109],"data":[18],"matrix":[19],"with":[20],"additional":[21],"sparsity":[22],"constraints":[23],"on":[24],"the":[25,56,62,67,81,85,110],"obtained":[26,96],"representation.":[27],"We":[28,46],"consider":[29],"two":[30],"probabilistic":[31],"formulations":[32],"sparse":[34,111],"PCA:":[35],"spiked":[37,40,43],"Wigner":[38],"and":[39,59],"Wishart":[41],"(or":[42],"covariance)":[44],"model.":[45],"analyze":[47],"an":[48,74],"Approximate":[49],"Message":[50],"Passing":[51],"(AMP)":[52],"algorithm":[53],"to":[54,92],"estimate":[55],"underlying":[57,86],"signal":[58],"show,":[60],"in":[61],"high":[63],"dimensional":[64],"limit,":[65],"that":[66,80],"AMP":[68],"estimates":[69],"are":[70],"information-theoretically":[71],"optimal.":[72],"As":[73],"immediate":[75],"corollary,":[76],"our":[77],"results":[78,102],"demonstrate":[79],"posterior":[82],"expectation":[83],"signal,":[87],"which":[88],"often":[90],"intractable":[91],"compute,":[93],"can":[94],"be":[95],"using":[97],"polynomial-time":[99],"scheme.":[100],"Our":[101],"also":[103],"effectively":[104],"provide":[105],"single-letter":[107],"characterization":[108],"PCA":[112],"problem.":[113]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}