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A comparative analysis of rank correlation measures for weakly-supervised learning

Grant number: 19/11104-8
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): September 01, 2019
Effective date (End): August 31, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Cooperation agreement: Microsoft Research
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Nikolas Gomes de Sá
Home Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Company:Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Instituto de Geociências e Ciências Exatas (IGCE)
Associated research grant:17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert, AP.PITE

Abstract

The rank correlation measures represent an effective way to encode contextual similarity information. Recently, such measures have been successfully exploited in various unsupervised learning tasks.In this scenario, this project considers the hypothesis that these measures can also be applied in weakly supervised learning tasks. The main idea consists in expanding small training sets through relationships with high values computed rank correlation measures. In this way, the main objective is to conduct a comparative study of different rank correlation measures which can be applied on weakly supervised learning methods.