| Full text | |
| 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 Opportunities: | 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 Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 20/08854-2 - Investigation of graph-based contextual measures for weakly-supervised learning |
| Grantee: | Bionda Rozin |
| Support Opportunities: | 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 Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |