What is the relationship between SVD and factor analysis? How can use singular values and other matrices from SVD to perform factor analysis or cluster document-term matrix without using other clustering techniques?
Google brought me here, and I dislike how the comments just assume everyone knows that FA and PCA are related. So to answer your question: yes. See Tipping and Bishop, 1996. This paper is great because:
- It discusses the connection between FA and PCA (Section 2.2)
- It discusses using the SVD to compute the ML parameters (Appendix A)
Google brought me here too, and I found that the implementation of Scikit-learn library, a famous repository for data science in Python, uses SVDs with a small tweak to fit the data points and perform factor analysis.
Hence the answer is a big YES you can use SVD.
If you're keen with code implementation, I suggest you can read the Factor Analysis source code of Scikit-learn here at github. They implement the SVD algorithm using Scipy library and tweak the output for shape adjustment.
In addition to that, I want to add some reference on top of Probabilistic Principal Component Analysis paper PPCA paper suggested by @gwg: