Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data
This work addresses the challenge of interpreting non-linear projections for data analysts, though it appears incremental as it builds on existing augmentation techniques.
The paper tackles the problem of explaining non-linear embeddings of high-dimensional data by introducing NoLiES, a tool that uses rangesets for attribute-based visualization, enabling users to observe structure and detect outliers in case studies with complex data distributions.
Embeddings of high-dimensional data are widely used to explore data, to verify analysis results, and to communicate information. Their explanation, in particular with respect to the input attributes, is often difficult. With linear projects like PCA the axes can still be annotated meaningfully. With non-linear projections this is no longer possible and alternative strategies such as attribute-based color coding are required. In this paper, we review existing augmentation techniques and discuss their limitations. We present the Non-Linear Embeddings Surveyor (NoLiES) that combines a novel augmentation strategy for projected data (rangesets) with interactive analysis in a small multiples setting. Rangesets use a set-based visualization approach for binned attribute values that enable the user to quickly observe structure and detect outliers. We detail the link between algebraic topology and rangesets and demonstrate the utility of NoLiES in case studies with various challenges (complex attribute value distribution, many attributes, many data points) and a real-world application to understand latent features of matrix completion in thermodynamics.