Sparse probabilistic projections
Cedric Archambeau and Francis Bach
In: NIPS 2008, Vancouver, Canada(2009).
We present a generative model for performing sparse probabilistic projections,
which includes sparse principal component analysis and sparse canonical correlation
analysis as special cases. Sparsity is enforced by means of automatic relevance
determination or by imposing appropriate prior distributions, such as generalised
hyperbolic distributions. We derive a variational Expectation-Maximisation
algorithm for the estimation of the hyperparameters and show that our novel probabilistic
approach compares favourably to existing techniques. We illustrate how
the proposed method can be applied in the context of cryptoanalysis as a preprocessing
tool for the construction of template attacks.