PCA is a prototypical linear dimensionality reduction technique with a closed-form solution , chapter 12). t-SNE is a non-linear, iterative method which is successful at maintaining local structure, meaning that points which are close together in high dimensional space remain close in the lower dimensional projection . Global structure is not necessarily preserved, and points that were far apart in high dimensional space may be brought closer together in projected space. An advantage of this is that the projections tend to be compact and easy to visualise. PCA and t-SNE were applied to feature configs 'B' to 'G', where config 'G' was first flattened into a 1D vector. Data was shuffled to remove order-related biases. PCA and t-SNE were implemented using scikit-learn.