Discriminative clustering of high-dimensional data using generative modeling

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dc.contributor.authorAbdi, M.
dc.contributor.authorLim, C.
dc.contributor.authorMohamed, S.
dc.contributor.authorNahavandi, S.
dc.contributor.authorAbbasnejad, E.
dc.contributor.authorVan Den Hengel, A.
dc.contributor.conferenceIEEE International Midwest Symposium on Circuits and Systems (MWSCAS) (5 Aug 2018 - 8 Aug 2018 : Windsor, Canada)
dc.date.issued2019
dc.description.abstractWe 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.
dc.description.statementofresponsibilityMasoud Abdi, Chee Peng Lim, Shady Mohamed, Saeid Nahavandi, Ehsan Abbasnejad, Anton Van Den Hengel
dc.identifier.citationThe ... Midwest Symposium on Circuits and Systems conference proceedings : MWSCAS. Midwest Symposium on Circuits and Systems, 2019, vol.2018-August, pp.799-802
dc.identifier.doi10.1109/MWSCAS.2018.8623970
dc.identifier.isbn9781538673928
dc.identifier.issn1548-3746
dc.identifier.issn1558-3899
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]
dc.identifier.urihttp://hdl.handle.net/2440/119596
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofseriesMidwest Symposium on Circuits and Systems Conference Proceedings
dc.rights©2018 IEEE
dc.source.urihttps://doi.org/10.1109/mwscas.2018.8623970
dc.subjectClustering; unsupervised learning; generative adversarial network; variational autoencoder; deep learning
dc.titleDiscriminative clustering of high-dimensional data using generative modeling
dc.typeConference paper
pubs.publication-statusPublished

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