Visualizing Time Dependent Data Using Dynamic t SNE
Many interesting processes can be represented as time dependent datasets. We define a time dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high dimensional real vectors) and each observation has a corresponding observation across time steps. Dimensionality reduction provides a scalable alternative to create visualizations (projections) that enable insight into the structure of such datasets. However, applying dimensionality reduction independently for each dataset in a sequence may introduce unnecessary variability in the resulting sequence of projections, which makes tracking the evolution of the data significantly more challenging. We show that this issue affects t SNE, a widely used dimensionality reduction technique. In this context, we propose dynamic t SNE, an adaptation of t SNE that introduces a controllable trade off between temporal coherence and projection reliability. Our evaluation in two time dependent datasets shows that dynamic t SNE eliminates unnecessary temporal variability and encourages smooth changes between projections.
EuroVis