t-SNE is used very widely in ML, both to analyze input data and to analyze learned representations. To me, the canonical example of people using t-SNE this way

t-SNE is used very widely in ML, both to analyze input data and to analyze learned representations. To me, the canonical example of people using t-SNE this way is visualizing word embeddings, like this: http://metaoptimize.s3.amazonaws.com/cw-embeddings-ACL2010/embeddings-mostcommon.EMBEDDING_SIZE=50.png http://metaoptimize.s3.amazonaws.com/cw-embeddings-ACL2010/e... Also, some lovely examples of using t-SNE to visualize conv net representations: http://cs.stanford.edu/people/karpathy/cnnembed/ http://cs.stanford.edu/people/karpathy/cnnembed/ I think people have found it very useful as one of the main tools for understanding what deep models are doing (along with optimization-based feature visualization) and to just check that your model is learning. I haven't really heard about people using it to pick input features, but that's probably mostly because I don't really work with anyone doing feature engineering.