{"id":"https://openalex.org/W2603741279","doi":"https://doi.org/10.1109/tit.2017.2686880","title":"Partial Hard Thresholding","display_name":"Partial Hard Thresholding","publication_year":2017,"publication_date":"2017-03-27","ids":{"openalex":"https://openalex.org/W2603741279","doi":"https://doi.org/10.1109/tit.2017.2686880","mag":"2603741279"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2017.2686880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2017.2686880","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-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/A5034432097","display_name":"Prateek Jain","orcid":"https://orcid.org/0000-0002-8191-9785"},"institutions":[{"id":"https://openalex.org/I4210124949","display_name":"Microsoft Research (India)","ror":"https://ror.org/02w7f3w92","country_code":"IN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210124949"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Prateek Jain","raw_affiliation_strings":["Microsoft Research India, Bangalure, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research India, Bangalure, India","institution_ids":["https://openalex.org/I4210124949"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051918150","display_name":"Ambuj Tewari","orcid":"https://orcid.org/0000-0001-6969-7844"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ambuj Tewari","raw_affiliation_strings":["Department of Statistics and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0001-6969-7844","affiliations":[{"raw_affiliation_string":"Department of Statistics and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063459703","display_name":"Inderjit S. Dhillon","orcid":"https://orcid.org/0000-0002-2759-1416"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Inderjit S. Dhillon","raw_affiliation_strings":["Department of Computer Science, The University of Texas at Austin, Austin, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034432097"],"corresponding_institution_ids":["https://openalex.org/I4210124949"],"apc_list":null,"apc_paid":null,"fwci":1.9753,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.84470247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"63","issue":"5","first_page":"3029","last_page":"3038"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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/T11996","display_name":"Random lasers and scattering media","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3102","display_name":"Acoustics and Ultrasonics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.8389960527420044},{"id":"https://openalex.org/keywords/restricted-isometry-property","display_name":"Restricted isometry property","score":0.7684727907180786},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.595635175704956},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.5913794040679932},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.568821907043457},{"id":"https://openalex.org/keywords/matching-pursuit","display_name":"Matching pursuit","score":0.5566709637641907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5224132537841797},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.4928419589996338},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4598347544670105},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4549448490142822},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4122447371482849},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.3908193111419678},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2822112441062927},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17984938621520996}],"concepts":[{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.8389960527420044},{"id":"https://openalex.org/C17902559","wikidata":"https://www.wikidata.org/wiki/Q17099734","display_name":"Restricted isometry property","level":3,"score":0.7684727907180786},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.595635175704956},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.5913794040679932},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.568821907043457},{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.5566709637641907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5224132537841797},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.4928419589996338},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4598347544670105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4549448490142822},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4122447371482849},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.3908193111419678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2822112441062927},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17984938621520996},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tit.2017.2686880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2017.2686880","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1484715564","https://openalex.org/W1578080815","https://openalex.org/W1977520307","https://openalex.org/W2015418199","https://openalex.org/W2021302824","https://openalex.org/W2025223969","https://openalex.org/W2055064119","https://openalex.org/W2082029531","https://openalex.org/W2091893102","https://openalex.org/W2106294397","https://openalex.org/W2108034412","https://openalex.org/W2116437043","https://openalex.org/W2118838680","https://openalex.org/W2122469757","https://openalex.org/W2128659236","https://openalex.org/W2129131372","https://openalex.org/W2160979406","https://openalex.org/W2167077875","https://openalex.org/W2289917018","https://openalex.org/W2963322354","https://openalex.org/W3124617746","https://openalex.org/W4250955649","https://openalex.org/W6677681297","https://openalex.org/W6678385449"],"related_works":["https://openalex.org/W1595179898","https://openalex.org/W2252480727","https://openalex.org/W2098521117","https://openalex.org/W2080115547","https://openalex.org/W4319793311","https://openalex.org/W1974198688","https://openalex.org/W1848313300","https://openalex.org/W1759790807","https://openalex.org/W3015194228","https://openalex.org/W2381127329"],"abstract_inverted_index":{"We":[0],"study":[1],"iterative":[2,93,132,252,293],"algorithms":[3,94,126,131],"for":[4,92,213,277],"compressed":[5],"sensing":[6],"that":[7,30,53,150,206,246],"have":[8,31,236],"an":[9],"\u201corthogonalization\u201d":[10],"step":[11],"at":[12,141,177],"each":[13,178],"iteration":[14,179],"to":[15,20,56,87,171,204,235],"keep":[16],"the":[17,21,27,38,57,63,67,76,103,109,142,160,172,175,182,185,197,209,219,227,249,264,278,283,291],"residual":[18],"orthogonal":[19,153],"span":[22],"of":[23,26,42,117,122,125,174,218,248,271,288,290],"those":[24,101],"columns":[25],"measurement":[28,228],"matrix":[29],"been":[32],"selected":[33],"so":[34],"far.":[35],"To":[36],"unify":[37],"design":[39],"and":[40,79,136,274,298],"analysis":[41,287],"such":[43,254],"algorithms,":[44,253],"we":[45,82,112,145,151,244,281],"propose":[46,113],"a":[47,84,114,147,224,268],"novel":[48,115,148,273],"partial":[49],"hard-thresholding":[50,104],"(PHT)":[51],"operator":[52,60,78,97],"is":[54,233],"similar":[55],"hard":[58,129,137],"thresholding":[59,130,133,138],"but":[61],"restricts":[62],"amount":[64],"by":[65],"which":[66],"support":[68,89,173],"set":[69],"can":[70,260],"change":[71,201],"in":[72,216],"one":[73,120,169,194],"iteration.":[74],"Using":[75],"PHT":[77,110,265,279],"its":[80],"properties,":[81],"provide":[83,282],"general":[85],"framework":[86],"prove":[88,205],"recovery":[90,215],"results":[91,276],"employing":[95,102],"this":[96],"as":[98,100,255],"well":[99],"operator.":[105,266],"Next,":[106],"based":[107,180],"on":[108,181,226],"operator,":[111,280],"family":[116,124],"algorithms.":[118],"At":[119],"end":[121],"our":[123,272],"lie":[127],"well-known":[128],"with":[134,156,184],"inversion":[135],"pursuit,":[139,258,297],"whereas":[140],"other":[143],"end,":[144],"get":[146],"algorithm":[149,163],"call":[152],"matching":[154],"pursuit":[155],"replacement":[157],"(OMPR).":[158],"Like":[159],"classic":[161],"greedy":[162],"OMP,":[164,190,259],"OMPR":[165,191,207],"too":[166],"adds":[167],"exactly":[168],"coordinate":[170,195],"iterate":[176],"correlation":[183],"current":[186],"residual.":[187],"However,":[188],"unlike":[189],"also":[192],"removes":[193],"from":[196],"support.":[198],"This":[199],"simple":[200],"allows":[202],"us":[203],"has":[208],"best":[210],"known":[211,234,285],"guarantees":[212,240],"sparse":[214],"terms":[217],"restricted":[220],"isometry":[221],"property":[222],"(RIP),":[223],"condition":[225],"matrix.":[229],"In":[230],"contrast,":[231],"OMP":[232],"very":[237],"weak":[238],"performance":[239],"under":[241],"RIP.":[242],"Finally,":[243],"show":[245],"most":[247],"existing":[250],"\u201corthogonalized\u201d":[251],"CoSaMP,":[256,295],"subspace":[257,296],"be":[261],"expressed":[262],"using":[263],"As":[267],"pleasing":[269],"consequence":[270],"generic":[275],"tightest":[284],"RIP":[286],"all":[289],"above-mentioned":[292],"algorithms:":[294],"OMP.":[299]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
