Contribution to Graph-based Multi-view Clustering: Algorithms and Applications

185 p.

Detalles Bibliográficos
Autor: El Hajjar, Sally
Tipo de recurso: tesis doctoral
Fecha de publicación:2022
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/59377
Acceso en línea:http://hdl.handle.net/10810/59377
Access Level:acceso abierto
Palabra clave:algorithmic languages
artificial intelligence
codes and coding systems
lenguajes algorítmicos
inteligencia artificial
códigos y sistemas de codificación
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spelling Contribution to Graph-based Multi-view Clustering: Algorithms and ApplicationsEl Hajjar, Sallyalgorithmic languagesartificial intelligencecodes and coding systemslenguajes algorítmicosinteligencia artificialcódigos y sistemas de codificación185 p.In this thesis, we study unsupervised learning, specifically, clustering methods for dividing data into meaningful groups. One major challenge is how to find an efficient algorithm with low computational complexity to deal with different types and sizes of datasets.For this purpose, we propose two approaches. The first approach is named "Multi-view Clustering via Kernelized Graph and Nonnegative Embedding" (MKGNE), and the second approach is called "Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding" (MVCGE). These two approaches jointly solve four tasks. They jointly estimate the unified similarity matrix over all views using the kernel tricks, the unified spectral projection of the data, the clusterindicator matrix, and the weight of each view without additional parameters. With these two approaches, there is no need for any postprocessing such as k-means clustering.In a further study, we propose a method named "Multi-view Spectral Clustering via Constrained Nonnegative Embedding" (CNESE). This method can overcome the drawbacks of the spectral clustering approaches, since they only provide a nonlinear projection of the data, on which an additional step of clustering is required. This can degrade the quality of the final clustering due to various factors such as the initialization process or outliers. Overcoming these drawbacks can be done by introducing a nonnegative embedding matrix which gives the final clustering assignment. In addition, some constraints are added to the targeted matrix to enhance the clustering performance.In accordance with the above methods, a new method called "Multi-view Spectral Clustering with a self-taught Robust Graph Learning" (MCSRGL) has been developed. Different from other approaches, this method integrates two main paradigms into the one-step multi-view clustering model. First, we construct an additional graph by using the cluster label space in addition to the graphs associated with the data space. Second, a smoothness constraint is exploited to constrain the cluster-label matrix and make it more consistent with the data views and the label view.Moreover, we propose two unified frameworks for multi-view clustering in Chapter 9. In these frameworks, we attempt to determine a view-based graphs, the consensus graph, the consensus spectral representation, and the soft clustering assignments. These methods retain the main advantages of the aforementioned methods and integrate the concepts of consensus and unified matrices. By using the unified matrices, we enforce the matrices of different views to be similar, and thus the problem of noise and inconsistency between different views will be reduced.Extensive experiments were conducted on several public datasets with different types and sizes, varying from face image datasets, to document datasets, handwritten datasets, and synthetics datasets. We provide several analyses of the proposed algorithms, including ablation studies, hyper-parameter sensitivity analyses, and computational costs. The experimental results show that the developed algorithms through this thesis are relevant and outperform several competing methods.Dornaika, Fadi2023202320222022info:eu-repo/semantics/doctoralThesisapplication/pdfhttp://hdl.handle.net/10810/59377reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/semantics/openAccess(c)2022 SALLY EL HAJJARoai:addi.ehu.eus:10810/593772026-06-18T09:23:17Z
dc.title.none.fl_str_mv Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
title Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
spellingShingle Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
El Hajjar, Sally
algorithmic languages
artificial intelligence
codes and coding systems
lenguajes algorítmicos
inteligencia artificial
códigos y sistemas de codificación
title_short Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
title_full Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
title_fullStr Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
title_full_unstemmed Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
title_sort Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
dc.creator.none.fl_str_mv El Hajjar, Sally
author El Hajjar, Sally
author_facet El Hajjar, Sally
author_role author
dc.contributor.none.fl_str_mv Dornaika, Fadi
dc.subject.none.fl_str_mv algorithmic languages
artificial intelligence
codes and coding systems
lenguajes algorítmicos
inteligencia artificial
códigos y sistemas de codificación
topic algorithmic languages
artificial intelligence
codes and coding systems
lenguajes algorítmicos
inteligencia artificial
códigos y sistemas de codificación
description 185 p.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/59377
url http://hdl.handle.net/10810/59377
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
(c)2022 SALLY EL HAJJAR
eu_rights_str_mv openAccess
rights_invalid_str_mv (c)2022 SALLY EL HAJJAR
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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