{"id":"https://openalex.org/W4289830182","doi":"https://doi.org/10.1109/spcom55316.2022.9840826","title":"On the Effective Sample Complexity for Exact Sparse Recovery from Sequential Linear Measurements","display_name":"On the Effective Sample Complexity for Exact Sparse Recovery from Sequential Linear Measurements","publication_year":2022,"publication_date":"2022-07-11","ids":{"openalex":"https://openalex.org/W4289830182","doi":"https://doi.org/10.1109/spcom55316.2022.9840826"},"language":"en","primary_location":{"id":"doi:10.1109/spcom55316.2022.9840826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spcom55316.2022.9840826","pdf_url":null,"source":{"id":"https://openalex.org/S4363608579","display_name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-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/A5014495966","display_name":"Samrat Mukhopadhyay","orcid":"https://orcid.org/0000-0002-2978-9565"},"institutions":[{"id":"https://openalex.org/I189109744","display_name":"Indian Institute of Technology Dhanbad","ror":"https://ror.org/013v3cc28","country_code":"IN","type":"education","lineage":["https://openalex.org/I189109744"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Samrat Mukhopadhyay","raw_affiliation_strings":["Indian Institute of Technology (Indian School of Mines),Department of Electronics Engineering,Dhanbad","Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology (Indian School of Mines),Department of Electronics Engineering,Dhanbad","institution_ids":["https://openalex.org/I189109744"]},{"raw_affiliation_string":"Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad","institution_ids":["https://openalex.org/I189109744"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5014495966"],"corresponding_institution_ids":["https://openalex.org/I189109744"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21926445,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"47","issue":null,"first_page":"1","last_page":"5"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9959999918937683,"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/iterated-function","display_name":"Iterated function","score":0.7761722803115845},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5814351439476013},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5725671052932739},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.5694982409477234},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4955652952194214},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4951702654361725},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"Sample complexity","score":0.46308714151382446},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.44405853748321533},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4185223877429962},{"id":"https://openalex.org/keywords/time-complexity","display_name":"Time complexity","score":0.418316513299942},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41700831055641174},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4152772128582001},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.36265748739242554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.09055808186531067},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.08627235889434814},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07355153560638428}],"concepts":[{"id":"https://openalex.org/C140479938","wikidata":"https://www.wikidata.org/wiki/Q5254619","display_name":"Iterated function","level":2,"score":0.7761722803115845},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5814351439476013},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5725671052932739},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5694982409477234},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4955652952194214},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4951702654361725},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.46308714151382446},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.44405853748321533},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4185223877429962},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.418316513299942},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41700831055641174},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4152772128582001},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36265748739242554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.09055808186531067},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.08627235889434814},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07355153560638428},{"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/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/spcom55316.2022.9840826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spcom55316.2022.9840826","pdf_url":null,"source":{"id":"https://openalex.org/S4363608579","display_name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W143004564","https://openalex.org/W1977520307","https://openalex.org/W2053692685","https://openalex.org/W2108982210","https://openalex.org/W2115706991","https://openalex.org/W2138500679","https://openalex.org/W2145096794","https://openalex.org/W2160536217","https://openalex.org/W2164452299","https://openalex.org/W2276204684","https://openalex.org/W2344857408","https://openalex.org/W2508031406","https://openalex.org/W2762625016","https://openalex.org/W2787248994","https://openalex.org/W2963322354","https://openalex.org/W4229706427","https://openalex.org/W4250955649"],"related_works":["https://openalex.org/W2158224665","https://openalex.org/W2379589510","https://openalex.org/W2810730439","https://openalex.org/W4300044672","https://openalex.org/W1881631164","https://openalex.org/W2358292267","https://openalex.org/W2378166785","https://openalex.org/W2737338842","https://openalex.org/W3005946484","https://openalex.org/W1973065909"],"abstract_inverted_index":{"In":[0],"this":[1,134,172],"paper":[2],"we":[3,51,111],"consider":[4],"the":[5,22,36,42,54,62,70,76,80,90,102,106,123,126,129,138,141,145,181],"problem":[6],"of":[7,10,64,101,105,128,147,188,196],"exact":[8,91],"recovery":[9,189,203],"a":[11,27,98],"fixed":[12,60,208],"sparse":[13,57,92],"vector":[14,38,58,93],"from":[15],"sequentially":[16],"arriving":[17],"measurements.":[18],"We":[19,66,169],"assume":[20,52],"that":[21,53,68,137,140,180,199],"measurements":[23,161,197],"are":[24,47,73,162],"generated":[25],"by":[26,79,190],"linear":[28],"model":[29],"with":[30,94,175,204,207],"time":[31,46,63,107,167],"varying":[32,108],"matrices":[33,72],"and":[34,125],"both":[35],"measurement":[37,71,209],"as":[39,41],"well":[40],"matrix":[43],"at":[44,144,164],"each":[45],"made":[48],"available.":[49],"However,":[50],"underlying":[55],"unknown":[56,130],"is":[59,150,153],"during":[61],"interest.":[65],"prove":[67],"if":[69,97,157],"i.i.d.":[74],"subGaussian,":[75],"iterates":[77],"produced":[78],"popular":[81],"iterative":[82],"hard":[83],"thresholding":[84],"(IHT)":[85],"algorithm":[86],"can":[87,184],"converge":[88],"to":[89],"high":[95],"probability":[96,139,187],"certain":[99,117],"function":[100],"sample":[103,114],"complexities":[104],"measurements,":[109],"which":[110,178],"call":[112],"effective":[113],"complexity":[115],"satisfies":[116],"lower":[118],"bound":[119,135],"dependent":[120],"on":[121],"K,N,":[122],"sparsity":[124],"length":[127],"vector,":[131],"respectively.":[132],"Interestingly,":[133],"reveals":[136],"estimation":[142],"error":[143],"end":[146],"some":[148],"instant":[149],"small":[151,159],"enough,":[152],"hardly":[154],"affected":[155],"even":[156],"very":[158],"number":[160,195],"used":[163],"sporadically":[165],"chosen":[166],"instances.":[168],"also":[170],"corroborate":[171],"theoretical":[173],"result":[174],"numerical":[176],"experiments":[177],"demonstrate":[179],"conventional":[182],"IHT":[183,206],"enjoy":[185],"greater":[186],"occasionally":[191],"using":[192],"far":[193],"lesser":[194],"than":[198],"required":[200],"for":[201],"successful":[202],"offline":[205],"matrix.":[210]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
