Deep unsupervised clustering with Gaussian mixture variational autoencoders
File(s)
Working paper
Author(s)
Type
Working Paper
Abstract
We study a variant of the variational autoencoder model with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the standard variational approach in these models is unsuited for unsupervised clustering, and mitigate this problem by leveraging a principled information-theoretic regularisation term known as consistency violation. Adding this term to the standard variational optimisation objective yields networks with both meaningful internal representations and well-defined clusters. We demonstrate the performance of this scheme on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving higher performance on unsupervised clustering classification than previous approaches.
Date Issued
2016-11-08
Citation
2016
Copyright Statement
© 2016 The Authors.
Identifier
http://arxiv.org/abs/1611.02648v1
Subjects
cs.LG
cs.LG
cs.NE
stat.ML