# A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration

arXiv: https://arxiv.org/abs/1811.12199 · HTML: https://mlsanity.com/paper/1811.12199/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 15 of 15)

- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Evaluating the Interpretability of Generative Models by Interactive Reconstruction](https://mlsanity.com/paper/2102.01264/) (2021)
- [Interactive Dimensionality Reduction for Comparative Analysis](https://mlsanity.com/paper/2106.15481/) (2021)
- [UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data](https://mlsanity.com/paper/2111.01744/) (2021)
- [Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data](https://mlsanity.com/paper/2108.08706/) (2021)
- [HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters](https://mlsanity.com/paper/2106.13777/) (2021)
- [Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis](https://mlsanity.com/paper/2302.01241/) (2023)
- [Augmenting Decision Making via Interactive What-If Analysis](https://mlsanity.com/paper/2109.06160/) (2021)
- [Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction](https://mlsanity.com/paper/2303.09590/) (2023)
- [Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices](https://mlsanity.com/paper/2506.04553/) (2025)
- [DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine](https://mlsanity.com/paper/2402.06885/) (2024)
- [Metric Design != Metric Behavior: Improving Metric Selection for the Unbiased Evaluation of Dimensionality Reduction](https://mlsanity.com/paper/2507.02225/) (2025)
- [ExClus: Explainable Clustering on Low-dimensional Data Representations](https://mlsanity.com/paper/2111.03168/) (2021)
- [InfoClus: Informative Clustering of High-dimensional Data Embeddings](https://mlsanity.com/paper/2504.11089/) (2025)
- [From Interaction to Intent: Inferring User Objectives from Provenance Logs](https://mlsanity.com/paper/2607.04501/) (2026)

## Abstract (arXiv)

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.