t-SNE
t-SNE is a non-linear dimensionality reduction technique that is well suited as a visualization technique as its goal is to reduce the dimensionality to 2 or 3 dimensions. The ideal goal of visualization is to build a map in low dimensional space that reflects the similarities in the original space. Popular dimensionality reduction techniques like PCA are not very useful for this purpose because the goal of PCA is to maximize variance which is the same as trying to minimize the squared error between distances in the original data and the map and hence this leads to preserving large pairwise distance in the map. However large pairwise distances are not always reliable and on the other small distances between neighbors is reliable. Techniques relying upon preserving neighboring distance are Isomap, LLE, SNE and t-SNE. t-SNE is based upon the SNE (stochastic neighbor embedding) algorithm and places a Gaussian around every point in the high dimensional space and then measure the density