Approximated and User Steerable tSNE for Progressive Visual Analytics

TL;DR

This paper presents A-tSNE, a controllable approximation of tSNE that enables real-time, interactive high-dimensional data visualization by balancing speed and accuracy, with user steerability and visual feedback.

Contribution

It introduces a novel controllable tSNE approximation that allows interactive exploration and user steering during data analysis.

Findings

Real-time visualization of high-dimensional data achieved

User can steer approximation levels during analysis

Effective for streaming and large datasets

Abstract

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.