Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/24969 https://doi.org/10.1109/CONTROLO.2018.8514541 |
Resumo: | This paper concerns the estimation of multiple dynamical models from a set of observed trajectories. It proposes vector valued gaussian random fields, representing dynamical models and their vector fields, combined with a modified k- means clustering algorithm to assign observed trajectories to models. The assignment is done according to a likelihood function obtained from applying the random field associated to a cluster, to the data. The algorithm is shown to have several advantages when compared with others: 1) it does not depend on a grid, region of interest, grid resolution or interpolation method; 2) the estimated vector fields has an associated uncertainty which is given by the algorithm and taken into account. The paper presents results obtained on synthetic trajectories that illustrate the performance of the proposed algorithm. |
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Clustering of Gaussian Random Vector Fields in Multiple Trajectory ModellingClusteringRandom Vector FieldsThis paper concerns the estimation of multiple dynamical models from a set of observed trajectories. It proposes vector valued gaussian random fields, representing dynamical models and their vector fields, combined with a modified k- means clustering algorithm to assign observed trajectories to models. The assignment is done according to a likelihood function obtained from applying the random field associated to a cluster, to the data. The algorithm is shown to have several advantages when compared with others: 1) it does not depend on a grid, region of interest, grid resolution or interpolation method; 2) the estimated vector fields has an associated uncertainty which is given by the algorithm and taken into account. The paper presents results obtained on synthetic trajectories that illustrate the performance of the proposed algorithm.2019-02-26T16:56:51Z2019-02-262018-06-04T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://hdl.handle.net/10174/24969http://hdl.handle.net/10174/24969https://doi.org/10.1109/CONTROLO.2018.8514541porBarão M., Marques J. S., "Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling", In proceedings of the 13th APCA International Conference on Automatic Control and Soft Computing, June 4-6, 2018, Azores, Portugalhttps://ieeexplore.ieee.org/document/8514541simnaonaomjsb@uevora.ptjsm@isr.ist.utl.pt498Barão, MiguelMarques, Jorge Salvadorinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:18:37Zoai:dspace.uevora.pt:10174/24969Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:15:34.973551Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling |
title |
Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling |
spellingShingle |
Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling Barão, Miguel Clustering Random Vector Fields |
title_short |
Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling |
title_full |
Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling |
title_fullStr |
Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling |
title_full_unstemmed |
Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling |
title_sort |
Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling |
author |
Barão, Miguel |
author_facet |
Barão, Miguel Marques, Jorge Salvador |
author_role |
author |
author2 |
Marques, Jorge Salvador |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Barão, Miguel Marques, Jorge Salvador |
dc.subject.por.fl_str_mv |
Clustering Random Vector Fields |
topic |
Clustering Random Vector Fields |
description |
This paper concerns the estimation of multiple dynamical models from a set of observed trajectories. It proposes vector valued gaussian random fields, representing dynamical models and their vector fields, combined with a modified k- means clustering algorithm to assign observed trajectories to models. The assignment is done according to a likelihood function obtained from applying the random field associated to a cluster, to the data. The algorithm is shown to have several advantages when compared with others: 1) it does not depend on a grid, region of interest, grid resolution or interpolation method; 2) the estimated vector fields has an associated uncertainty which is given by the algorithm and taken into account. The paper presents results obtained on synthetic trajectories that illustrate the performance of the proposed algorithm. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-04T00:00:00Z 2019-02-26T16:56:51Z 2019-02-26 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/24969 http://hdl.handle.net/10174/24969 https://doi.org/10.1109/CONTROLO.2018.8514541 |
url |
http://hdl.handle.net/10174/24969 https://doi.org/10.1109/CONTROLO.2018.8514541 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Barão M., Marques J. S., "Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling", In proceedings of the 13th APCA International Conference on Automatic Control and Soft Computing, June 4-6, 2018, Azores, Portugal https://ieeexplore.ieee.org/document/8514541 sim nao nao mjsb@uevora.pt jsm@isr.ist.utl.pt 498 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136637580476416 |