A Geometry-Based Approach to Visualize High-Dimensional Data
Caio Flexa, Walisson Cardoso Gomes, Sergio Viademonte, Claudomiro Sales, Ronnie Cley de Oliveira Alves · 2019
Big Data has attracted extensive attention from industry, academia and governments around the world, employing various approaches from many fields such as machine learning, pattern recognition and data visualization. Data visualization is quite useful in the perception of relevant information by a human for gaining understanding and insight from data with high dimensionality. This paper presents a novel approach for dimensionality reduction called Polygonal Coordinate System (PCS), which is able to represent multi-dimensional data into a two-dimensional data. For this purpose, data are represented across a regular polygon or interface between the high dimensions and the 2D plane. PCS can deal with massive data sets by adopting an incremental and efficient dimensionality reduction. Statistical comparison using Spearman's rho correlation highlights the utility of PCS, outperforming the state-of-the-art t-Distributed Stochastic Neighbor Embedding (t-SNE) technique.