Investigation and evaluation of rank correlation measures

• Grant number: 21/07993-1
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2021
• End date: August 31, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Daniel Carlos Guimarães Pedronette
• Grantee: Vinicius Atsushi Sato Kawai
• 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

Similarity information based on contextual analysis has great potential for various machine learning and information retrieval tasks. Rank correlation measures provide an effective tool for contextual similarity analysis. Recently, these measures have been successfully exploited in unsupervised and weakly supervised domains. In this scenario, the project aims to evaluate and investigate novel measures. The main idea consists of evaluating existing measures and investigating the development of metrics that can increase the effectiveness of the methods in which they are used. (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)

VALEM, LUCAS PASCOTTI; SATO KAWAI, VINICIUS ATSUSHI; PEREIRA-FERRERO, VANESSA HELENA; GUIMARAES PEDRONETTE, DANIEL CARLOS; IEEE. A NOVEL RANK CORRELATION MEASURE FOR MANIFOLD LEARNING ON IMAGE RETRIEVAL AND PERSON RE-ID. 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, v. N/A, p. 5-pg., 2022-01-01. (18/15597-6, 20/02183-9, 21/07993-1, 20/11366-0)