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
- It seems that if we have more than 50 features, it is better to work with
PCAand not with
T-SNE, Did I understand it correctly ?
- Why T-SNE is not good with high number of features ?
- Why the document suggest to work with
PCAand not with other dimension-reduction (like