Discriminative clustering of high-dimensional data using generative modeling
Date
2019
Authors
Abdi, M.
Lim, C.
Mohamed, S.
Nahavandi, S.
Abbasnejad, E.
Van Den Hengel, A.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
The ... Midwest Symposium on Circuits and Systems conference proceedings : MWSCAS. Midwest Symposium on Circuits and Systems, 2019, vol.2018-August, pp.799-802
Statement of Responsibility
Masoud Abdi, Chee Peng Lim, Shady Mohamed, Saeid Nahavandi, Ehsan Abbasnejad, Anton Van Den Hengel
Conference Name
IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) (5 Aug 2018 - 8 Aug 2018 : Windsor, Canada)
Abstract
We approach unsupervised clustering from a generative perspective. We hybridize Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) in a novel way to obtain a vigorous clustering model that can effectively be applied to challenging high-dimensional datasets. The powerful inference of the VAE is used along with a categorical discriminator that aims to obtain a cluster assignment of the data, by maximizing the mutual information between the observations and their predicted class distribution. The discriminator is regularized with examples produced by an adversarial generator, whose task is to trick the discriminator into accepting them as real data. We demonstrate that using a shared latent representation greatly helps with discriminative power of our model and leads to a powerful unsupervised clustering model. The method can be applied to raw data in a high-dimensional space. Training can be performed end-to-end from randomly-initialized weights by alternating stochastic gradient descent on the parameters of the model. Experiments on two datasets including the challenging MNIST dataset show that the proposed method performs better than the existing models. Additionally, our method yields an efficient generative model.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
©2018 IEEE