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JMLR
2012

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models

6 years 9 months ago
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models
This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such parameterizations of models for binary data are known, including the Ising, generalized Ising, canonical and full parameterizations. We also discuss a parameterization that we call the “spectral parameterization”, which has received significantly less coverage in existing literature. We provide this parameterization with a spectral interpretation by casting loglinear models in terms of orthogonal WalshHadamard harmonic expansions. Using various standard and group sparse regularizers for structural learning, we provide a comprehensive theoretical and empirical comparison of these parameterizations. We show that the spectral parameterization, along with the canonical, has the best performance and sparsity levels, while the spectral does not depend on any particular reference state. The spectral interpretation also provides a new starting point for analyzing th...
David Buchman, Mark W. Schmidt, Shakir Mohamed, Da
Added 27 Sep 2012
Updated 27 Sep 2012
Type Journal
Year 2012
Where JMLR
Authors David Buchman, Mark W. Schmidt, Shakir Mohamed, David Poole, Nando de Freitas
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