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Sparse probabilistic projections
Cedric Archambeau · Francis Bach

Wed Dec 10 07:30 PM -- 12:00 AM (PST) @

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 pre-processing tool for the construction of template attacks.

Author Information

Cedric Archambeau (Helsing)
Francis Bach (INRIA - Ecole Normale Superieure)

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