ExplorerTree: a focus+context exploration approach for 2D embeddings

TL;DR

ExplorerTree is a multilevel visualization approach that reduces clutter in 2D embeddings of high-dimensional data, enabling effective exploration through focus+context interaction and sampling techniques.

Contribution

It introduces a novel scatter plot-based multilevel method with focus+context interaction for large datasets, addressing visualization clutter in 2D embeddings.

Findings

Effective reduction of visual clutter in large datasets

Enhanced exploration of neural network activation images

User experiment confirms improved embedding structure comprehension

Abstract

In exploratory tasks involving high-dimensional datasets, dimensionality reduction (DR) techniques help analysts to discover patterns and other useful information. Although scatter plot representations of DR results allow for cluster identification and similarity analysis, such a visual metaphor presents problems when the number of instances of the dataset increases, resulting in cluttered visualizations. In this work, we propose a scatter plot-based multilevel approach to display DR results and address clutter-related problems when visualizing large datasets, together with the definition of a methodology to use focus+context interaction on non-hierarchical embeddings. The proposed technique, called ExplorerTree, uses a sampling selection technique on scatter plots to reduce visual clutter and guide users through exploratory tasks. We demonstrate ExplorerTree's effectiveness through a use case, where we visually explore activation images of the convolutional layers of a neural network. Finally, we also conducted a user experiment to evaluate ExplorerTree's ability to convey embedding structures using different sampling strategies.