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| Author(s): |
Rodrigo Colnago Contreras
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2020-05-07 |
| Examining board members: |
Luis Gustavo Nonato;
Maria Cristina Ferreira de Oliveira;
José Gustavo de Souza Paiva;
Emanuele Marques dos Santos
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| Advisor: | Luis Gustavo Nonato |
| Abstract | |
Graph learning is a branch of deep learning in which the intention is to estimate a graph that describes a network where the edges correspond to relationships between the most similar elements represented by the nodes of the network. Graph learning techniques have been developed in recent years by the most important researchers in the field of signal processing on graphs. However, to the best of our knowledge, its use in visual analytics has yet not been explored. In this work, we propose the use of graph learning methods in an application, in which a high number of networks are generated to facilitate the perception of patterns present in these networks, through a new visual analytics tool, called NE-Motion. The tool developed is applied to a database formed by thousands of time series. The database has been provided by medical professionals from New York University, who are specialists in studies of people who have had a stroke. The proposed methodology and visualization tool were able to reveal information present in the data while depicting it in an intuitive way to the experts, who attested to the effectiveness of our approach. (AU) | |
| FAPESP's process: | 15/14358-0 - Using Symmetries for Visual Analytics of Massive Data |
| Grantee: | Rodrigo Colnago Contreras |
| Support Opportunities: | Scholarships in Brazil - Doctorate |