Here on the following picture, we can identify two clouds. We can identify a lot of points that are out of the two clouds and can disrupt a bit the the training and test accuracy (%). Is there an unsupervised machine learning model or other techniques we can use to get rid of those points?
I thought to try to train a neural network to maximize inter class distances, but minimize intra class distances, but it is a bit unclear.
To explain the image here, I have over 76 features I pass to a LSTM model. The graph axis you see are the three first principal components (i.e. related to PCA). The red dots are -1 labels and blue dots are the 1 labels.