I have a large data set (around 4600 rows and 10000 columns). First, I performed L2 normalization and did PCA to obtained 50 components. Then I performed t-SNE and obtained 2 components. I did the clustering of my samples with t-SNE, which looks nice; i.e. t-SNE is able to capture the clusters present in the data. Now I want to use these t-SNE components to train ML algorithms for classification and regression task. My question is:
1- Do we need to normalize these t-SNE components before fed into any ML algorithms 2- I want to append additional features to these components. Do I need to rescale these additional features with t-SNE components?
https://stats.stackexchange.com/questions/369224/t-sne-on-principal-component-scores-standardization-needed ( great post) helped me to understand why PCA components should not be normalized before t-SNE. Can we apply the same reasoning for my question 1? What about the second one? Could you please help me to understand this?