Visual metric learning using kernels

• Grant number: 14/11296-0
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: July 21, 2014
• End date: July 20, 2015
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Antonio Castelo Filho
• Grantee: Douglas Cedrim Oliveira
• Supervisor: Eduard Gröller
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: Vienna University of Technology (TU Wien), Austria
• Associated to the scholarship: 11/12263-0 - Manifold reconstruction from point clouds, BP.DR

Abstract

This research project aims to perform an Interactive Visual Analysis of data embedded on high-dimensional feature-spaces. The main goal of this project is to enable the user with interactive resources towards facilitating the metric learning process. The proposed methodology relies on the pre-imageand kernel theory supported by visualization resources. (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)

CEDRIM, DOUGLAS; VAD, VIKTOR; PAIVA, AFONSO; GROELIER, M. EDUARD; NONATO, LUIS GUSTAVO; CASTELO, ANTONIO. Depth functions as a quality measure and for steering multidimensional projections. COMPUTERS & GRAPHICS-UK, v. 60, p. 93-106, NOV 2016. (11/12263-0, 14/11296-0, 11/22749-8, 14/09546-9)