Mixture of GANs for Clustering

For data clustering, Gaussian mixture model (GMM) is a typical method that trains several Gaussian models to capture the data. Each Gaussian model then provides the distribution information of a cluster. For clustering of high dimensional and complex data, more flexible models rather than Gaussian models are desired. Recently, the generative adversarial networks (GANs) have shown effectiveness in capturing complex data distribution. Therefore, GAN mixture model (GANMM) would be a promising alternative of GMM. However, we notice that the non-flexibility of the Gaussian model is essential in the expectation-maximization procedure for training GMM. GAN can have much higher flexibility, which disables the commonly employed expectation-maximization procedure, as that the maximization cannot change the result of the expectation. In this paper, we propose to use the epsilon-expectation-maximization procedure for training GANMM. The experiments show that the proposed GANMM can have good performance on complex data as well as simple data.

• Publication year

  2018

• Venue

  International Joint Conference on Artificial Intelligence

• Publication date

  2018-07-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.24963/ijcai.2018/423
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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