I'm trying to do Embedded clustering using kmeans.
This is customer data, so it involves a lot of sentences, so I'm using the universal sentence encoder before clustering.
But I should be doing a feature selection or dimensionality reduction before embedding the features.
I want to know if there is a method to do feature selection or dimensionality reduction in unsupervised learning. This could be very helpful as the clustering is giving a mixed result as of now and I have a strong feeling that this could be because of the unwanted attribute in the data.
I have read all the resources which only gave options to be done on the supervised learning.
Any help is appreciated!