2008
NeurIPS
NeurIPS 2008
Sparse probabilistic projections
Abstract
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.
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Trend Setter
- Variational Inference
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Hot Topic Early Bird
- generative model
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Representation Learning
Deep Learning > Models > Variational Inference
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Bayesian & Probabilistic > Variational Inference
Keywords
probabilistic modeling
variational inference
principal component analysis
expectation maximization
sparse principal component analysis
automatic relevance determination
canonical correlation analysis
sparse probabilistic projections
generative model
probabilistic model
sparse projection
variational expectation-maximization
sparse canonical correlation analysis
probabilistic projection