  Classical methods such as Principal Component Analysis (PCA) and Canonical
Correlation Analysis (CCA) are ubiquitous in statistics. However, these
techniques are only able to reveal linear relationships in data. Although
nonlinear variants of PCA and CCA have been proposed, these are computationally
prohibitive in the large scale.
  In a separate strand of recent research, randomized methods have been
proposed to construct features that help reveal nonlinear patterns in data. For
basic tasks such as regression or classification, random features exhibit
little or no loss in performance, while achieving drastic savings in
computational requirements.
  In this paper we leverage randomness to design scalable new variants of
nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such
as spectral clustering or LDA. We demonstrate our algorithms through
experiments on real-world data, on which we compare against the
state-of-the-art. A simple R implementation of the presented algorithms is
provided.
