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).
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2 Answers
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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.
answered Mar 12, 2020 at 20:58
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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.
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$\begingroup$ Yes, and in case of face recognition? Can i map them to skin color or race ? For example asian euroean and African? $\endgroup$ Mar 15, 2020 at 18:31
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$\begingroup$ It is not typically used like this (see other comments), BUT you could use TSNE/UMAP/PCA + Clustering to make a more informed decision on how to map your labels to a simpler set. $\endgroup$ Sep 1, 2020 at 21:37