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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?

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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.

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

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