Hands-On Machine Learning for Algorithmic Trading

t-SNE

The t-distributed stochastic neighbor embedding is an award-winning algorithm developed in 2010 by Laurens van der Maaten and Geoff Hinton to detect patterns in high-dimensional data. It takes a probabilistic, non-linear approach to locating data on several different but related low-dimensional manifolds.

The algorithm emphasizes keeping similar points together in low dimensions, as opposed to maintaining the distance between points that are apart in high dimensions, which results from algorithms such as PCA that minimize squared distances.

The algorithm proceeds by converting high-dimensional distances to (conditional) probabilities, where high probabilities imply low distance and reflect the likelihood of sampling two points based ...