t-SNE number of output components Why do most tSNE implementations suggest using 2 or 3 output dimensions?
For PCA, the number of output components is typically choosen based on the number of components needed to explain 80% of variance in data. Can something similar be done for tSNE?

A downside of t-SNE is that it does not give an equation for transforming data from the high dimension to the low dimension. Thus, you cannot do something like transform out-of-sample (test/validation) data using the feature extraction derived from the training data. Other dimension reduction methods, such as PCA, do allow you to apply a transformation to the test data (apply the eigenvalue matrix to the out-of-sample data, even though the eigenvalues were calculated from the covariance matrix of the in-sample data).
Consequently, t-SNE is most useful as a visualization tool, not as a feature extraction method for supervised learning.
Guess which dimensions are the dimensions where we like to do visualization?
