I would like to extract features from (without loss of generality) numerical data using unsupervised learning methods among these:
- transformations: PCA/ICA/NMF
- embeddings: T-distributed stochastic neighbor embedding.
- cluster based methods: k-means or similar
- kernel based: kernel PCA
I also think about using auto-encoders or similar. The extracted feature are then used in a classifier.
My question: I study each of these methods one by one. Some in the original context (e.g. clustering) and some in the context of feature extraction. I lack experience on the details and many questions arise as
- Can I stack these methods? What do I lose?
- Can I apply them on subsets (to reduce training time) of the data and predict on the rest?
Are there tutorials/lecture notes/blog posts on the web that describe best practice of feature extraction in this sense?
PS: Courses like this Week 4: Feature construction deal with my question - I would love to see more examples from an applied point of view. This question Tutorials for feature engineering is similar but I hope mine is not a duplicate.