# Approximated and User Steerable tSNE for Progressive Visual Analytics

arXiv: https://arxiv.org/abs/1512.01655 · HTML: https://mlsanity.com/paper/1512.01655/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2015.

## Built on by (most cited first, 19 of 19)

- [Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data](https://mlsanity.com/paper/2105.07536/) (2021)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters](https://mlsanity.com/paper/2106.13777/) (2021)
- [Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization](https://mlsanity.com/paper/2207.12214/) (2022)
- [EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models](https://mlsanity.com/paper/2406.17462/) (2024)
- [Deep Recursive Embedding for High-Dimensional Data](https://mlsanity.com/paper/2104.05171/) (2021)
- [Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction](https://mlsanity.com/paper/2110.00317/) (2021)
- [Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location](https://mlsanity.com/paper/2110.00809/) (2021)
- [CUPID: Contextual Understanding of Prompt-conditioned Image Distributions](https://mlsanity.com/paper/2406.07699/) (2024)
- [Navigating Perplexity: A linear relationship with the data set size in t-SNE embeddings](https://mlsanity.com/paper/2308.15513/) (2023)
- [Explaining the Power of Topological Data Analysis in Graph Machine Learning](https://mlsanity.com/paper/2401.04250/) (2024)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl](https://mlsanity.com/paper/2108.09478/) (2021)
- [Deep Recursive Embedding for High-Dimensional Data](https://mlsanity.com/paper/2111.00622/) (2021)
- [Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images](https://mlsanity.com/paper/2202.09179/) (2022)
- [Improving multidimensional projection quality with user-specific metrics and optimal scaling](https://mlsanity.com/paper/2407.16328/) (2024)
- [Manifold-Preserving Superpixel Hierarchies and Embeddings for the Exploration of High-Dimensional Images](https://mlsanity.com/paper/2602.24160/) (2026)
- [A Multi-Attribute Latent Space for Visual Analysis of Watches](https://mlsanity.com/paper/2606.27897/) (2026)
- [Human Activity Recognition via Ultra-Wideband Data: A Framework for Dimensionality Reduction, Pattern Discovery, and Predictive Modeling](https://mlsanity.com/paper/2609.32008/) (2026)

## Abstract (arXiv)

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis.