This library will eventually implement variants of the random kitchen sinks feature generation algorithm for nonlinear modeling. It will depend on the Armadilllo linear algebra library. I may eventually wish to extend an interface to python (numpy).

my goal is to implement:

• Random Fourier Features (Gaussian)
• Fastfood features (Gaussian, Polynomial)
• Orthogonal Random Features (Gaussian)
• Structures Orthogonal Random Features (Gaussian)
• ... and whatever I may come accross in the literature.

the following references should be of interest:

Rahimi, Ali and Benjamin Recht (2008). “Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning”. In: Advances in neural information processing systems, pp. 1313–1320.

^ this is the third paper in a trilogy

Felix, X Yu et al. (2016). “Orthogonal random features”. In: Advances in Neural Information Processing Systems, pp. 1975–1983.

Quoc Le, Tam ́as Sarl ́os and Alex Smola (2013). “Fastfood-approximating kernel expansions in loglinear time”. In: Proceedings of the international conference on machine learning vol. 85.

Halko, Nathan, Per-Gunnar Martinsson, and Joel A. Tropp (2009). “Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions”. In: SIAM vol. 53, pp. 217–288.