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arXiv · 1401.8017

Sparse Bayesian Unsupervised Learning

Stephane Gaiffas·Bertrand Michel
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Abstract

This paper is about variable selection, clustering and estimation in an unsupervised high-dimensional setting. Our approach is based on fitting constrained Gaussian mixture models, where we learn the number of clusters $K$ and the set of relevant variables $S$ using a generalized Bayesian posterior with a sparsity inducing prior. We prove a sparsity oracle inequality which shows that this procedure selects the optimal parameters $K$ and $S$. This procedure is implemented using a Metropolis-Hastings algorithm, based on a clustering-oriented greedy proposal, which makes the convergence to the posterior very fast.

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Stephane Gaiffas, Bertrand Michel. 2014-01-30. Sparse Bayesian Unsupervised Learning. https://arxiv.org/abs/1401.8017

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