Understanding interactive multidimensional projections

• Ano de defesa: 2016
• Autor(a) principal: Fadel, Samuel Gomes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Dimensionality reduction; Information visualization; Redução de dimensionalidade; Visualização de informação
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16012017-095849/

The large amount of available data on a diverse range of human activities provides many opportunities for understanding, improving and revealing unknown patterns in them. Powerful automatic methods for extracting this knowledge from data are already available from machine learning and data mining. They, however, rely on the expertise of analysts to improve their results when those are not satisfactory. In this context, interactive multidimensional projections are a useful tool for the analysis of multidimensional data by revealing their underlying structure while allowing the user to manipulate the results to provide further insight into this structure. This manipulation, however, has received little attention regarding their influence on the mappings, as they can change the final layout in unpredictable ways. This is the main motivation for this research: understanding the effects caused by changes in these mappings. We approach this problem from two perspectives. First, the user perspective, we designed and developed visualizations that help reduce the trial and error in this process by providing the right piece of information for performing manipulations. Furthermore, these visualizations help explain the changes in the map caused by such manipulations. Second, we defined the effectiveness of manipulation in quantitative terms, then developed an experimental framework for assessing manipulations in multidimensional projections under this view. This framework is based on improving mappings using known evaluation measures for these techniques. Using the improvement of measures as different types of manipulations, we perform a series of experiments on five datasets, five measures, and four techniques. Our experimental results show that there are possible types of manipulations that can happen effectively, with some techniques being more susceptible to manipulations than others.