Data Visualization using Functional Data Analysis

Haozhe Chen, Andres Felipe Duque Correa, Guy Wolf, Kevin R. Moon

Session: General

Keywords: Functional Data Analysis, dynamical processes, data denoising, dimensionality reduction, data visualization

Abstract: Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many existing methods fail to capture the underlying structure of the data. Furthermore, existing methods that can theoretically eliminate all noise are difficult to implement in high dimensions. Here we propose a new data visualization method called Functional Information Geometry (FIG) for dynamical processes that denoises the data by leveraging time information and mitigates the curse of dimensionality using approaches from functional data analysis. We experimentally demonstrate that FIG outperforms other methods in terms of capturing the true structure, hyperparameter robustness, and computational speed. We then use our method to visualize EEG brain measurements of sleep activity.

Submission Number: 127