Authors:
Mateus Espadoto
1
;
Nina S. T. Hirata
1
;
Alexandre X. Falcão
2
and
Alexandru C. Telea
3
Affiliations:
1
Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
;
2
Institute of Computing, University of Campinas, Campinas, Brazil
;
3
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Keyword(s):
Dimensionality Reduction, Machine Learning, Neural Networks, Multidimensional Projections.
Abstract:
Dimensionality reduction methods are often used to explore multidimensional data in data science and information visualization. Techniques of the SNE-class, such as t-SNE, have become the standard for data exploration due to their good visual cluster separation, but are computationally expensive and don’t have out-of-sample capability by default. Recently, a neural network-based technique was proposed, which adds out-of-sample capability to t-SNE with good results, but with the disavantage of introducing some diffusion of the points in the result. In this paper we evaluate many neural network-tuning strategies to improve the results of this technique. We show that a careful selection of network architecture, loss function and data augmentation strategy can improve results.