乐胖代购免代理版

{"id":"https://openalex.org/W1976486660","doi":"https://doi.org/10.1109/icassp.2014.6854649","title":"Distributed least mean squares strategies for sparsity-aware estimation over Gaussian Markov random fields","display_name":"Distributed least mean squares strategies for sparsity-aware estimation over Gaussian Markov random fields","publication_year":2014,"publication_date":"2014-05-01","ids":{"openalex":"https://openalex.org/W1976486660","doi":"https://doi.org/10.1109/icassp.2014.6854649","mag":"1976486660"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6854649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5000852147","display_name":"Paolo Di Lorenzo","orcid":"https://orcid.org/0000-0002-4130-3177"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Di Lorenzo","raw_affiliation_strings":["DIET \u2013 Sapienza University of Rome, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"DIET \u2013 Sapienza University of Rome, Rome, Italy","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084159640","display_name":"Sergio Barbarossa","orcid":"https://orcid.org/0000-0001-9846-8741"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Sergio Barbarossa","raw_affiliation_strings":["DIET \u2013 Sapienza University of Rome, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"DIET \u2013 Sapienza University of Rome, Rome, Italy","institution_ids":["https://openalex.org/I861853513"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.638,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":14,"citation_normalized_percentile":{"value":0.597366,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":89},"biblio":{"volume":null,"issue":null,"first_page":"5472","last_page":"5476"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9997,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9997,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997,"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":0.9971,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.4459739}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6554881},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6394584},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.60745555},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.56815284},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.55493146},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47185513},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.47003454},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.4459739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36591396},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3457019},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3396071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30581817},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29142043},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19073859},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08082363},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6854649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":24,"referenced_works":["https://openalex.org/W1498711961","https://openalex.org/W1525535255","https://openalex.org/W1595020299","https://openalex.org/W1603765807","https://openalex.org/W1965392255","https://openalex.org/W1970997001","https://openalex.org/W1986302543","https://openalex.org/W2071607622","https://openalex.org/W2074796812","https://openalex.org/W2098674248","https://openalex.org/W2104266187","https://openalex.org/W2112694609","https://openalex.org/W2115671189","https://openalex.org/W2119604302","https://openalex.org/W2121820607","https://openalex.org/W2130359405","https://openalex.org/W2130442323","https://openalex.org/W2135046866","https://openalex.org/W2141613549","https://openalex.org/W2153966940","https://openalex.org/W2296616510","https://openalex.org/W250076511","https://openalex.org/W4250955649","https://openalex.org/W4388323202"],"related_works":["https://openalex.org/W987019958","https://openalex.org/W4233015508","https://openalex.org/W2413828414","https://openalex.org/W2379651310","https://openalex.org/W2367222340","https://openalex.org/W2113019827","https://openalex.org/W2078379274","https://openalex.org/W2030382593","https://openalex.org/W1983122994","https://openalex.org/W1541249122"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3],"propose":[4],"distributed":[5,29],"strategies":[6,63],"for":[7,64],"the":[8,49,57,61],"estimation":[9],"of":[10,60,67],"sparse":[11,68],"vectors":[12],"over":[13],"adaptive":[14],"networks.":[15],"The":[16],"measurements":[17],"collected":[18],"at":[19],"different":[20],"nodes":[21],"are":[22],"assumed":[23],"to":[24,31],"be":[25],"spatially":[26],"correlated":[27],"and":[28],"according":[30],"a":[32],"Gaussian":[33],"Markov":[34],"random":[35],"field":[36],"(GMRF)":[37],"model.":[38],"We":[39],"derive":[40],"optimal":[41],"sparsity-aware":[42],"algorithms":[43],"that":[44],"incorporate":[45],"prior":[46],"information":[47],"about":[48],"statistical":[50],"dependency":[51],"among":[52],"observations.":[53],"Simulation":[54],"results":[55],"show":[56],"potential":[58],"advantages":[59],"proposed":[62],"online":[65],"recovery":[66],"vectors.":[69]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W1976486660","counts_by_year":[{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":1}],"updated_date":"2024-12-08T00:28:20.597889","created_date":"2016-06-24"}