# T-SNE with high number of features

• If we have high number of features (more than 50), should we use T-SNE ?

• It is highly recommended to use another dimensionality reduction method (e.g. PCA for dense data or TruncatedSVD for sparse data) to reduce the number of dimensions to a reasonable amount (e.g. 50) if the number of features is very high. This will suppress some noise and speed up the computation of pairwise distances between samples

1. It seems that if we have more than 50 features, it is better to work with PCA and not with T-SNE, Did I understand it correctly ?
2. Why T-SNE is not good with high number of features ?
3. Why the document suggest to work with PCA and not with other dimension-reduction (like UMAP) ?
• t-SNE works well with much more than 50 features. In NLP research, it is usual to see it applied to hundreds of features. However, in general, UMAP is better than t-SNE for any purpose, at least in my experience; probably UMAP is not mentioned in the t-SNE docs because they were written before its existence.
– noe
Jun 22 '21 at 7:03