I have 96 features and the labels are represented by 1 and -1 for inputting to a deep learning model.
Here the 3 axis represent the 3 first principal components. The blue cloud represents the labels 1 and the red cloud represents the labels -1.
Even if we can identify two different clouds visually, they are stuck together. I think we can face a problem during the training phase because of that.
For the same features and labels with t-SNE, we can still distinguish two clouds, but again they are stuck together.
1- Does the fact that the two clouds of dots are stuck together affect the % accuracy during the training and testing phase?
2- When we remove the red and blue color, we have somehow only one big cloud. Is there a way to work around the problem of the two clouds being stuck together?