variational information bottleneck for unsupervised clustering: deep gaussian mixture embedding

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ID: 132130
2020
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Abstract
In this paper, we develop an unsupervised generative clustering framework that combines the variational information bottleneck and the Gaussian mixture model. Specifically, in our approach, we use the variational information bottleneck method and model the latent space as a mixture of Gaussians. We derive a bound on the cost function of our model that generalizes the Evidence Lower Bound (ELBO) and provide a variational inference type algorithm that allows computing it. In the algorithm, the coders’ mappings are parametrized using neural networks, and the bound is approximated by Markov sampling and optimized with stochastic gradient descent. Numerical results on real datasets are provided to support the efficiency of our method.
Reference Key
uur2020entropyvariational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yiğit Uğur;George Arvanitakis;Abdellatif Zaidi
Journal European journal of medicinal chemistry
Year 2020
DOI
10.3390/e22020213
URL
https://www.mdpi.com/1099-4300/22/2/213
Keywords
clustering unsupervised learning gaussian mixture model

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