{"id":"https://openalex.org/W2751430773","doi":"https://doi.org/10.1109/wsom.2017.8020020","title":"Adaptive basis functions for prototype-based classification of functional data","display_name":"Adaptive basis functions for prototype-based classification of functional data","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2751430773","doi":"https://doi.org/10.1109/wsom.2017.8020020","mag":"2751430773"},"language":"en","primary_location":{"id":"doi:10.1109/wsom.2017.8020020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsom.2017.8020020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.rug.nl/ws/files/47383551/Adaptive_Basis_Functions_for_Prototype_based.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018753288","display_name":"Gabriele Bani","orcid":null},"institutions":[{"id":"https://openalex.org/I122346577","display_name":"University of Modena and Reggio Emilia","ror":"https://ror.org/02d4c4y02","country_code":"IT","type":"education","lineage":["https://openalex.org/I122346577"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Gabriele Bani","raw_affiliation_strings":["University of Modena and Reggio Emilia, 41125 Modena, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Modena and Reggio Emilia, 41125 Modena, Italy","institution_ids":["https://openalex.org/I122346577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035286804","display_name":"Udo Seiffert","orcid":"https://orcid.org/0000-0002-6043-7947"},"institutions":[{"id":"https://openalex.org/I4210139350","display_name":"Fraunhofer Institute for Factory Operation and Automation","ror":"https://ror.org/04qfaak15","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210139350","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Udo Seiffert","raw_affiliation_strings":["Fraunhofer Institute for Factory Operation and Automation IFF, Sandtorstrasse 22, 39106 Magdeburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Factory Operation and Automation IFF, Sandtorstrasse 22, 39106 Magdeburg, Germany","institution_ids":["https://openalex.org/I4210139350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083548477","display_name":"Michael Biehl","orcid":"https://orcid.org/0000-0001-5148-4568"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Michael Biehl","raw_affiliation_strings":["University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science, P.O. Box 407, 9700 AK Groningen, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science, P.O. Box 407, 9700 AK Groningen, The Netherlands","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079264070","display_name":"Friedrich Melchert","orcid":"https://orcid.org/0000-0002-9145-2277"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]},{"id":"https://openalex.org/I4210139350","display_name":"Fraunhofer Institute for Factory Operation and Automation","ror":"https://ror.org/04qfaak15","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210139350","https://openalex.org/I4923324"]}],"countries":["DE","NL"],"is_corresponding":false,"raw_author_name":"Friedrich Melchert","raw_affiliation_strings":["Fraunhofer Institute for Factory Operation and Automation IFF, Sandtorstrasse 22, 39106 Magdeburg, Germany","University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science, Groningen, AK, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Factory Operation and Automation IFF, Sandtorstrasse 22, 39106 Magdeburg, Germany","institution_ids":["https://openalex.org/I4210139350"]},{"raw_affiliation_string":"University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science, Groningen, AK, The Netherlands","institution_ids":["https://openalex.org/I169381384"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2041,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52797986,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T11236","display_name":"Control Systems and Identification","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10908","display_name":"Analytical Chemistry and Chromatography","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6766641139984131},{"id":"https://openalex.org/keywords/basis-function","display_name":"Basis function","score":0.6548584699630737},{"id":"https://openalex.org/keywords/functional-principal-component-analysis","display_name":"Functional principal component analysis","score":0.6485947966575623},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.6419915556907654},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5745148062705994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5095322728157043},{"id":"https://openalex.org/keywords/functional-data-analysis","display_name":"Functional data analysis","score":0.4817935824394226},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.467292845249176},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.45654749870300293},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.43913185596466064},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4261569380760193},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38747847080230713},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3428463041782379},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23445406556129456}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6766641139984131},{"id":"https://openalex.org/C5917680","wikidata":"https://www.wikidata.org/wiki/Q2621825","display_name":"Basis function","level":2,"score":0.6548584699630737},{"id":"https://openalex.org/C71176878","wikidata":"https://www.wikidata.org/wiki/Q17014987","display_name":"Functional principal component analysis","level":3,"score":0.6485947966575623},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.6419915556907654},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5745148062705994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5095322728157043},{"id":"https://openalex.org/C51820054","wikidata":"https://www.wikidata.org/wiki/Q5508814","display_name":"Functional data analysis","level":2,"score":0.4817935824394226},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.467292845249176},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.45654749870300293},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.43913185596466064},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4261569380760193},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38747847080230713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3428463041782379},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23445406556129456},{"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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/wsom.2017.8020020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsom.2017.8020020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.rug.nl:publications/f59823ea-6919-4860-b51b-3740f8d82554","is_oa":true,"landing_page_url":"https://research.rug.nl/en/publications/f59823ea-6919-4860-b51b-3740f8d82554","pdf_url":"https://pure.rug.nl/ws/files/47383551/Adaptive_Basis_Functions_for_Prototype_based.pdf","source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Bani, G, Seiffert, U, Biehl, M & Melchert, F 2017, Adaptive basis functions for prototype-based classification of functional data. in 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). IEEEXplore, pp. 1-8, 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), Nancy, France, 28/06/2017. https://doi.org/10.1109/WSOM.2017.8020020","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:fraunhofer.de:N-473786","is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-473786.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer IFF","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/399248","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/399248","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":{"id":"pmh:oai:pure.rug.nl:publications/f59823ea-6919-4860-b51b-3740f8d82554","is_oa":true,"landing_page_url":"https://research.rug.nl/en/publications/f59823ea-6919-4860-b51b-3740f8d82554","pdf_url":"https://pure.rug.nl/ws/files/47383551/Adaptive_Basis_Functions_for_Prototype_based.pdf","source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Bani, G, Seiffert, U, Biehl, M & Melchert, F 2017, Adaptive basis functions for prototype-based classification of functional data. in 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). IEEEXplore, pp. 1-8, 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), Nancy, France, 28/06/2017. https://doi.org/10.1109/WSOM.2017.8020020","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2751430773.pdf","grobid_xml":"https://content.openalex.org/works/W2751430773.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W25311418","https://openalex.org/W1576043040","https://openalex.org/W2036245684","https://openalex.org/W2039434802","https://openalex.org/W2093827448","https://openalex.org/W2094150678","https://openalex.org/W2097296149","https://openalex.org/W2103595817","https://openalex.org/W2111823723","https://openalex.org/W2123749980","https://openalex.org/W2126120145","https://openalex.org/W2134155547","https://openalex.org/W2143060555","https://openalex.org/W2146245281","https://openalex.org/W2274386229","https://openalex.org/W2558649365","https://openalex.org/W2580082328","https://openalex.org/W4213332169","https://openalex.org/W4234535302","https://openalex.org/W6634488672","https://openalex.org/W6678524678","https://openalex.org/W6681201575","https://openalex.org/W6681260687"],"related_works":["https://openalex.org/W2299080873","https://openalex.org/W273216563","https://openalex.org/W2605967135","https://openalex.org/W2056592727","https://openalex.org/W1925427981","https://openalex.org/W2963184067","https://openalex.org/W2340639288","https://openalex.org/W3124114881","https://openalex.org/W3169146846","https://openalex.org/W2085579733"],"abstract_inverted_index":{"We":[0,10],"present":[1],"a":[2,41,53,62,91,109,149],"framework":[3],"for":[4,59,148],"distance-based":[5],"classification":[6,84,90,124],"of":[7,14,23,37,44,51,56,66,88,130,134,152],"functional":[8,39,42,69,79,112],"data.":[9],"consider":[11],"the":[12,38,45,60,77,83,89,98,118,128,131,141],"analysis":[13,129],"labeled":[15],"spectral":[16],"data":[17,47,106,142],"and":[18,144],"time":[19],"series":[20],"by":[21,108],"means":[22],"Generalized":[24],"Matrix":[25],"Relevance":[26],"Learning":[27],"Vector":[28],"Quantization":[29],"(GMLVQ)":[30],"as":[31,102,104],"an":[32,67,146],"example.":[33],"To":[34],"take":[35],"advantage":[36],"nature":[40],"expansion":[43,61],"input":[46,100],"is":[48,71,74,94],"considered.":[49],"Instead":[50],"using":[52],"predefined":[54,111],"set":[55,133],"basis":[57,70,135],"functions":[58,136],"more":[63],"flexible":[64],"scheme":[65],"adaptive":[68],"employed.":[72],"GMLVQ":[73,92],"applied":[75,96],"on":[76,105],"resulting":[78],"parameters":[80],"to":[81,97,122],"solve":[82],"task.":[85],"For":[86],"comparison":[87],"system":[93],"also":[95],"raw":[99],"data,":[101],"well":[103],"expanded":[107],"different":[110],"basis.":[113],"Computer":[114],"experiments":[115],"show":[116],"that":[117],"methods":[119],"offers":[120],"potential":[121],"improve":[123],"performance":[125],"significantly.":[126],"Furthermore":[127],"adapted":[132],"give":[137],"further":[138],"insights":[139],"into":[140],"structure":[143],"yields":[145],"option":[147],"drastic":[150],"reduction":[151],"dimensionality.":[153]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
