Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Deep Adversarial Gaussian Mixture Auto-Encoder for Clustering
Warith Harchaoui, Pierre-Alexandre Mattei, Charles Bouveyron
Feb 15, 2017 (modified: Feb 15, 2017)ICLR 2017 workshop submissionreaders: everyone
Abstract:Feature representation for clustering purposes consists in building an explicit or implicit mapping of the input space onto a feature space that is easier to cluster or classify. This paper relies upon an adversarial auto-encoder as a means of building a code space of low dimensionality suitable for clustering. We impose a tunable Gaussian mixture prior over that space allowing for a simultaneous optimization scheme. We arrive at competitive unsupervised classification results on hand-written digits images (MNIST) that is customarily classified within a supervised framework.
TL;DR:We simultaneously train both an auto-encoder and a Gaussian mixture model for clustering in an adversarial fashion
Keywords:Deep learning, Unsupervised Learning
Enter your feedback below and we'll get back to you as soon as possible.