Cholesky-factorized sparse Kernel in support vector machines

Support Vector Machine (SVM) is one of the most powerful machine learning algorithms due to its convex optimization formulation and handling non-linear classification. However, one of its main drawbacks is the long time it takes to train large data sets. This limitation is often aroused when applyin...

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Detalles Bibliográficos
Autor: Abdellatif, Alhasan
Tipo de recurso: tesis de maestría
Fecha de publicación:2019
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/166187
Acceso en línea:https://hdl.handle.net/2117/166187
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Support Vector Machines
RBF Kernel
Sparse Kernel
Large data sets
Machine Learning
Intel·ligència artificial
Classificació AMS::68 Computer science::68T Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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oai_identifier_str oai:upcommons.upc.edu:2117/166187
network_acronym_str ES
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repository_id_str
spelling Cholesky-factorized sparse Kernel in support vector machinesAbdellatif, AlhasanArtificial intelligenceSupport Vector MachinesRBF KernelSparse KernelLarge data setsMachine LearningIntel·ligència artificialClassificació AMS::68 Computer science::68T Artificial intelligenceÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialSupport Vector Machine (SVM) is one of the most powerful machine learning algorithms due to its convex optimization formulation and handling non-linear classification. However, one of its main drawbacks is the long time it takes to train large data sets. This limitation is often aroused when applying non-linear kernels (e.g. RBF Kernel) which are usually required to obtain better separation for linearly inseparable data sets. In this thesis, we study an approach that aims to speed-up the training time by combining both the better performance of RBF kernels and fast training by a linear solver, LIBLINEAR. The approach uses an RBF kernel with a sparse matrix which is factorized using Cholesky decomposition. The method is tested on large artificial and real data sets and compared to the standard RBF and linear kernels where both the accuracy and training time are reported. For most data sets, the result shows a huge training time reduction, over 90\%, whilst maintaining the accuracy.Universitat Politècnica de CatalunyaCastro Pérez, JordiBelanche Muñoz, Luis Antonio20192019-07-0120192019-07-15master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/166187reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1661872026-05-27T15:37:01Z
dc.title.none.fl_str_mv Cholesky-factorized sparse Kernel in support vector machines
title Cholesky-factorized sparse Kernel in support vector machines
spellingShingle Cholesky-factorized sparse Kernel in support vector machines
Abdellatif, Alhasan
Artificial intelligence
Support Vector Machines
RBF Kernel
Sparse Kernel
Large data sets
Machine Learning
Intel·ligència artificial
Classificació AMS::68 Computer science::68T Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Cholesky-factorized sparse Kernel in support vector machines
title_full Cholesky-factorized sparse Kernel in support vector machines
title_fullStr Cholesky-factorized sparse Kernel in support vector machines
title_full_unstemmed Cholesky-factorized sparse Kernel in support vector machines
title_sort Cholesky-factorized sparse Kernel in support vector machines
dc.creator.none.fl_str_mv Abdellatif, Alhasan
author Abdellatif, Alhasan
author_facet Abdellatif, Alhasan
author_role author
dc.contributor.none.fl_str_mv Castro Pérez, Jordi
Belanche Muñoz, Luis Antonio
dc.subject.none.fl_str_mv Artificial intelligence
Support Vector Machines
RBF Kernel
Sparse Kernel
Large data sets
Machine Learning
Intel·ligència artificial
Classificació AMS::68 Computer science::68T Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Artificial intelligence
Support Vector Machines
RBF Kernel
Sparse Kernel
Large data sets
Machine Learning
Intel·ligència artificial
Classificació AMS::68 Computer science::68T Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description Support Vector Machine (SVM) is one of the most powerful machine learning algorithms due to its convex optimization formulation and handling non-linear classification. However, one of its main drawbacks is the long time it takes to train large data sets. This limitation is often aroused when applying non-linear kernels (e.g. RBF Kernel) which are usually required to obtain better separation for linearly inseparable data sets. In this thesis, we study an approach that aims to speed-up the training time by combining both the better performance of RBF kernels and fast training by a linear solver, LIBLINEAR. The approach uses an RBF kernel with a sparse matrix which is factorized using Cholesky decomposition. The method is tested on large artificial and real data sets and compared to the standard RBF and linear kernels where both the accuracy and training time are reported. For most data sets, the result shows a huge training time reduction, over 90\%, whilst maintaining the accuracy.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-07-01
2019
2019-07-15
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/166187
url https://hdl.handle.net/2117/166187
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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