T-SNE is widely used for visualising high-dimensional data in lower dimensions.
To reduce the costs of parameter optimisation, t-SNE is performed on a sample of the original data. After sampling the points, the distances between them need to be calculated, which is expensive due to the high dimensionality of the data. We apply Dijkstra's algorithm to approximate the distances and similarities between the sampled points. This reduces the algorithm's execution time by up to 40% without degrading the quality of the projections.