Factorization Machines (FMs) are a means to express the high dimensional data into lower dimensions, despite the original data being sparse.

How is it different from PCA which itself is a dimensionality reduction technique?

Are there pros-cons of either approach?


The main difference is that PCA is a dimensionality reduction technique, while Factorization Machines are classifiers. You can use PCA to simplify/compress a given dataset, while you can use FMs to classify your observations.

The other difference is that PCA is a linear technique, while FMs are non-linear. PCA extracts later factors that are linearly associated with your input variables, while FMs, on the other side, can "learn" non-linear patters in your dataset.

  • $\begingroup$ Can FactorizationMachines not be used just for dimensionality reduction? $\endgroup$ – sandyp Jun 10 '19 at 21:05

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.