voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data

TL;DR

voxel2vec is an unsupervised NLP-inspired model that learns low-dimensional representations of scalar values in scientific data, enabling analysis of complex spatial and temporal relationships.

Contribution

It introduces voxel2vec, a novel approach applying NLP techniques to learn distributed representations of scientific data features, capturing their contextual similarities.

Findings

Effective in representing scalar-value relationships

Improves feature classification accuracy

Enhances association analysis in scientific datasets

Abstract

Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex. This paper presents voxel2vec, a novel unsupervised representation learning model, which is used to learn distributed representations of scalar values/scalar-value combinations in a low-dimensional vector space. Its basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values/scalar-value combinations as symbols, voxel2vec learns the similarity between them in the context of spatial distribution and then allows us to explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and to association analysis for time-varying and ensemble data.