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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Rozin, Bionda [1] ; Pereira-Ferrero, Vanessa Helena [1] ; Lopes, Leonardo Tadeu [1] ; Pedronette, Daniel Carlos Guimaraes [1]
Total Authors: 4
Affiliation:
[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, Rio Claro - Brazil
Total Affiliations: 1
Document type: Journal article
Source: INFORMATION SCIENCES; v. 580, p. 202-220, NOV 2021.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
Grantee:João Paulo Papa
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 20/02183-9 - Rank-based unsupervised learning through deep learning in diverse domains
Grantee:Vanessa Helena Pereira Ferrero
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 20/08854-2 - Investigation of graph-based contextual measures for weakly-supervised learning
Grantee:Bionda Rozin
Support type: Scholarships in Brazil - Scientific Initiation
FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants - Phase 2