Rank-based unsupervised learning through deep learning in diverse domains

• Grant number: 20/02183-9
• Support Opportunities: Scholarships in Brazil - Post-Doctorate
• Effective date (Start): April 01, 2020
• Effective date (End): March 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Daniel Carlos Guimarães Pedronette
• Grantee: Vanessa Helena Pereira Ferrero
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
• Associated research grant: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks, AP.JP2

Abstract

Ranking-based unsupervised learning methods have been established as a solution to increase the effectiveness of content-based searches without requiring user intervention. These methods exploit contextual relationships among images, usually encoded in the distance/similarity information of the collections. Concomitantly, strategies based on deep learning have assumed a prominent position in the most diverse machine learning tasks. Thus, this research project aims to combine these two lines of research, investigating new unsupervised learning methods that are based on ranking information and deep learning. In addition, it is also intended to investigate other retrieval and classification scenarios, expanding the domains of application of unsupervised learning methods. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

ROZIN, BIONDA; PEREIRA-FERRERO, VANESSA HELENA; LOPES, LEONARDO TADEU; PEDRONETTE, DANIEL CARLOS GUIMARAES. A rank-based framework through manifold learning for improved clustering tasks. INFORMATION SCIENCES, v. 580, p. 202-220, NOV 2021. (17/25908-6, 20/02183-9, 20/08854-2, 18/15597-6)