A rank-based framework through manifold learning for improved clustering tasks

Autor(es)

Rozin, Bionda ^[1] ; Pereira-Ferrero, Vanessa Helena ^[1] ; Lopes, Leonardo Tadeu ^[1] ; Pedronette, Daniel Carlos Guimaraes ^[1]

Afiliação do(s) autor(es)

^[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, Rio Claro - Brazil

• Tipo de documento: Artigo Científico
• Fonte: INFORMATION SCIENCES; v. 580, p. 202-220, NOV 2021.
• Citações Web of Science: 0

Resumo

The relevance of diversified data preprocessing approaches for improving clustering tasks is remarkable. Once the effectiveness is direct impacted by feature representation and sim-ilarity definition, considerable attention from the research community has been drawn to this direction. More recently, rank-based manifold learning methods have been success-fully explored in unsupervised similarity learning for retrieval scenarios. Such methods consider the underlying dataset manifold to compute a new similarity measure, which increases the separability of data from distinct classes. In this paper, a rank-based frame-work for clustering tasks is proposed based on contemporary manifold learning methods. A flexible model is employed, where ranking structures are the representation of similarity information among data samples. Subsequently, is made the exploration of unsupervised similarity learning. It is also possible to compute more effective similarity measures and clustering results. To assess the effectiveness of the proposed framework was conducted a comprehensive experimental evaluation. The tests involved various public image data-sets, considering different manifold learning and clustering methods. The quantitative experiments take into consideration comparisons with traditional and recent state-of -the-art clustering approaches. (c) 2021 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 17/25908-6 - Aprendizado fracamente supervisionado para análise de vídeos no domínio comprimido em tarefas de recuperação e classificação para alertas visuais
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 20/02183-9 - Aprendizado não supervisionado baseado em ranqueamento e aprendizado profundo em domínios diversos
• Beneficiário: Vanessa Helena Pereira Ferrero
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 20/08854-2 - Investigação de medidas contextuais para aprendizado fracamente supervisionado baseadas em grafos
• Beneficiário: Bionda Rozin
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 18/15597-6 - Aplicação e investigação de métodos de aprendizado não-supervisionado em tarefas de recuperação e classificação
• Beneficiário: Daniel Carlos Guimarães Pedronette
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2