Ex: Matlab's t-sne tutorials frequently use PCA
" Process Data Using t-SNE
Obtain two-dimensional analogs of the data clusters using t-SNE. Use the Barnes-Hut algorithm for better performance on this large data set. Use PCA to reduce the initial dimensions from 784 to 50. <- (1) Why are we using PCA here to reduce dimensions to 50 first at all if we are going to use t-sne after PCA to reduce to 2 dimensions anyway?
Matlab Tutorial Code: https://www.mathworks.com/help/stats/tsne-settings.html
rng default % for reproducibility Y = tsne(X,'Algorithm','barneshut','NumPCAComponents',50); figure gscatter(Y(:,1),Y(:,2),L)
1) See question bolded above
2) What would you have googled to find this out?
I had googled "Why is PCA often used before t-sne for problems when the goal is only to reduce dimensionality? "