{"id":"https://openalex.org/W2561214334","doi":"https://doi.org/10.1109/eusipco.2016.7760413","title":"Computational analysis of a fast algorithm for high-order sparse linear prediction","display_name":"Computational analysis of a fast algorithm for high-order sparse linear prediction","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2561214334","doi":"https://doi.org/10.1109/eusipco.2016.7760413","mag":"2561214334"},"language":"en","primary_location":{"id":"doi:10.1109/eusipco.2016.7760413","is_oa":true,"landing_page_url":"https://doi.org/10.1109/eusipco.2016.7760413","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7760413","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 24th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7760413","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074156299","display_name":"Tobias Lindstr\u00f8m Jensen","orcid":"https://orcid.org/0000-0003-4262-0577"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Tobias Lindstrom Jensen","raw_affiliation_strings":["Signal and Information Processing, Aalborg Universitet, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Signal and Information Processing, Aalborg Universitet, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000691788","display_name":"Daniele Giacobello","orcid":"https://orcid.org/0000-0001-8708-8604"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniele Giacobello","raw_affiliation_strings":["Codec Technologies R&D, DTS Inc., Calabasas, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Codec Technologies R&D, DTS Inc., Calabasas, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073284331","display_name":"Toon van Waterschoot","orcid":"https://orcid.org/0000-0002-6323-7350"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Toon van Waterschoot","raw_affiliation_strings":["Department of Electrical Engineering, KU Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, KU Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026658144","display_name":"Mads Gr\u00e6sb\u00f8ll Christensen","orcid":"https://orcid.org/0000-0003-3586-7969"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Mads Grasboll Christensen","raw_affiliation_strings":["AD:MT, Aalborg Universitet, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AD:MT, Aalborg Universitet, Denmark","institution_ids":["https://openalex.org/I891191580"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1073","last_page":"1077"},"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/T11233","display_name":"Advanced Adaptive Filtering 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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9997000098228455,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.672382116317749},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.6662705540657043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6214002370834351},{"id":"https://openalex.org/keywords/interior-point-method","display_name":"Interior point method","score":0.5465008616447449},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.48256269097328186},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.47079411149024963},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.47033414244651794},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.46384671330451965},{"id":"https://openalex.org/keywords/convex-function","display_name":"Convex function","score":0.4605194330215454},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.44799911975860596},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.445738285779953},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.4417901337146759},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4352302551269531},{"id":"https://openalex.org/keywords/linear-system","display_name":"Linear system","score":0.41674888134002686},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.41126787662506104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30010560154914856},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.11070165038108826},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.10023993253707886}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.672382116317749},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.6662705540657043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6214002370834351},{"id":"https://openalex.org/C155253501","wikidata":"https://www.wikidata.org/wiki/Q461992","display_name":"Interior point method","level":2,"score":0.5465008616447449},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.48256269097328186},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.47079411149024963},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.47033414244651794},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.46384671330451965},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.4605194330215454},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.44799911975860596},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.445738285779953},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.4417901337146759},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4352302551269531},{"id":"https://openalex.org/C6802819","wikidata":"https://www.wikidata.org/wiki/Q1072174","display_name":"Linear system","level":2,"score":0.41674888134002686},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.41126787662506104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30010560154914856},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.11070165038108826},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.10023993253707886},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/eusipco.2016.7760413","is_oa":true,"landing_page_url":"https://doi.org/10.1109/eusipco.2016.7760413","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7760413","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 24th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire/c52567dc-869d-4423-943d-438bfb730dda","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/c52567dc-869d-4423-943d-438bfb730dda","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Jensen, T L, Giacobello, D, van Waterschoot, T & Christensen, M G 2016, Computational Analysis of a Fast Algorithm for High-order Sparse Linear Prediction. in Signal Processing Conference (EUSIPCO), 2016 24th European. IEEE (Institute of Electrical and Electronics Engineers), Proceedings of the European Signal Processing Conference (EUSIPCO), pp. 1073-1077, European Signal Processing Conference, Budapest, Hungary, 29/08/2016. https://doi.org/10.1109/EUSIPCO.2016.7760413","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/609680","is_oa":false,"landing_page_url":"https://lirias.kuleuven.be/bitstream/123456789/609680/1/16-74.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary, 28 August - 2 September 2016","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.1109/eusipco.2016.7760413","is_oa":true,"landing_page_url":"https://doi.org/10.1109/eusipco.2016.7760413","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7760413","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 24th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W12828664","https://openalex.org/W74720146","https://openalex.org/W210359992","https://openalex.org/W797708412","https://openalex.org/W1409984952","https://openalex.org/W1483629157","https://openalex.org/W1493163583","https://openalex.org/W1634005169","https://openalex.org/W1802969839","https://openalex.org/W1828907027","https://openalex.org/W1966264494","https://openalex.org/W1967073510","https://openalex.org/W1968735445","https://openalex.org/W1969128975","https://openalex.org/W1970467060","https://openalex.org/W1995319862","https://openalex.org/W1996287810","https://openalex.org/W2027928741","https://openalex.org/W2041165203","https://openalex.org/W2046795941","https://openalex.org/W2050834445","https://openalex.org/W2055507002","https://openalex.org/W2065030431","https://openalex.org/W2068641562","https://openalex.org/W2076495042","https://openalex.org/W2080585478","https://openalex.org/W2091360050","https://openalex.org/W2092663520","https://openalex.org/W2096359733","https://openalex.org/W2096819401","https://openalex.org/W2100556411","https://openalex.org/W2100705753","https://openalex.org/W2101986122","https://openalex.org/W2102182691","https://openalex.org/W2104765941","https://openalex.org/W2128119571","https://openalex.org/W2129516068","https://openalex.org/W2137213227","https://openalex.org/W2143749403","https://openalex.org/W2146482778","https://openalex.org/W2146973938","https://openalex.org/W2150620152","https://openalex.org/W2164278908","https://openalex.org/W2165291881","https://openalex.org/W2232609331","https://openalex.org/W2296319761","https://openalex.org/W2326591006","https://openalex.org/W2949483514","https://openalex.org/W4250589301","https://openalex.org/W4292363360","https://openalex.org/W4312258136","https://openalex.org/W6629017404","https://openalex.org/W6638824412","https://openalex.org/W6657299365","https://openalex.org/W6674652017","https://openalex.org/W6679248785"],"related_works":["https://openalex.org/W4378529241","https://openalex.org/W2483828597","https://openalex.org/W2094757704","https://openalex.org/W2059909660","https://openalex.org/W2617248812","https://openalex.org/W4285075998","https://openalex.org/W2918676124","https://openalex.org/W2510140549","https://openalex.org/W2187449906","https://openalex.org/W2962818859"],"abstract_inverted_index":{"Using":[0],"a":[1,105],"sparsity":[2],"promoting":[3],"convex":[4,80],"penalty":[5],"function":[6],"on":[7],"high-order":[8,81],"linear":[9,34,83],"prediction":[10,35,84,100],"coefficients":[11],"and":[12,23,61,95,102],"residuals":[13],"has":[14],"shown":[15],"to":[16,110],"result":[17],"in":[18,50],"improved":[19],"modeling":[20],"of":[21,32,64,74,99],"speech":[22],"other":[24],"signals":[25],"as":[26,114],"this":[27,38],"addresses":[28],"the":[29,59,65,79,90,96],"inherent":[30,91],"limitations":[31],"standard":[33],"methods.":[36,119],"However,":[37],"new":[39],"formulation":[40],"is":[41],"computationally":[42,115],"more":[43,116],"demanding":[44],"which":[45],"may":[46],"limit":[47],"its":[48],"use,":[49],"particular":[51],"for":[52,77],"embedded":[53],"signal":[54],"processing.":[55],"This":[56],"paper":[57,87],"analyzes":[58,89],"algorithmic":[60],"computational":[62],"aspects":[63],"matrix":[66],"structures":[67],"associated":[68],"with":[69],"an":[70],"alternating":[71],"direction":[72],"method":[73],"multipliers":[75],"algorithm":[76],"solving":[78],"sparse":[82],"problem.":[85],"The":[86],"also":[88],"trade-off":[92],"between":[93],"accuracy":[94],"objective":[97],"measure":[98],"gain":[101],"shows":[103],"that":[104],"few":[106],"iterations":[107],"are":[108],"sufficient":[109],"achieve":[111],"similar":[112],"results":[113],"expensive":[117],"interior-point":[118]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
