can I use t-sne or PCA to reduce number of classes?

Question

I wanted to know if I can use t-sne or PCA to reduce the number of classes depending on the similarity between them. For example, if I have 100  classes of 100 different animals and would like to put all the cats in a group and all the dogs in a group etc. (to get few groups of these 100 classes).

Brian Spiering · Accepted Answer

No. t-Distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) are dimension reduction techniques, aka fewer columns of a tidy dataframe.

Clustering will reduce the number of observations, aka fewer rows of a tidy dataframe. In particular, you might be looking for hierarchical clustering.

Michael Higgins · Answer

If you want to reduce the number of classes you are predicting over, then you could manually map them to a simpler set (i.e. map poodle, greyhound to dog ) OR if you don't have the domain knowledge you can cluster your data and predict the cluster instead of their original labels.

You could use PCA or t-SNE to reduce the number of dimensions before clustering.  This is best practice if you have many features (~>100) as you will often run into the curse of high dimensionality.

can I use t-sne or PCA to reduce number of classes?

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