Part 9 Extension to unsupervised learning

This interface could also be extended to unsupervised learning following the spirit of Scikit-Learn by replacing the predict method with transform in the unsupervised case.

Model families make sense in the unsupervised domain. For example, k-means and convex clustering both involve some sort of hyperparameter selection.

• For convex clustering you want to select a penalization parameter, probably optimizing one of several proposed clustering statistics
• For k-means you want to take the mode of cluster assignments, so transform(k_means) is probably a bad idea, but transform(k_means_family) would be interesting.

9.1 Invertible Mappings

I want a standard S3 method for invertible mappings in the spirit of scale/unscale. In some sense recipes does this, but only for the forward transformation. scale/unscale store information about the transformation in the returned object, which I’m not sold on. I’d prefer the following:

pca_model <- pca(X)                 # pca(X) wraps fit(new_pca(), X)
Z <- transform(pca_model, X)              # transform performs forward mapping
X_recovered <- untransform(pca_model, Z)  # untransform performs inverse mapping

and then you could provide the standard wrappers like fit_transform, fit_untransform, etc.

9.2 Unsupervised Transformations That Are Not Maps

It’s worth thinking about unsupervised techniques like t-SNE because you can transform data, but you can’t ever fit a (non-parametric) t-SNE model because t-SNE doesn’t define a function mapping data to a new space.

Offer a transform method for these but no fit?