An entangled mixture of variational autoencoders approach to deep clustering

We present a novel deep clustering algorithm that utilizes a variational autoencoder (VAE) framework with an entangled multi encoder-decoder neural architecture. Our model enforces a complementary structure that guides the learned latent representations towards a better space arrangement. It differs from previous VAE-based clustering algorithms by employing a new generative model that uses multiple encoder-decoders that are entangled to provide a joint clustering decision. The optimal clustering is found by optimizing a lower bound of the model likelihood function. Both the reconstruction component and the regularization component of the ELBO objective function are explicitly involved in the clustering procedure. We show that this modeling results in both better clustering capabilities and improved data generation. The proposed method is evaluated on standard datasets and is shown to significantly outperform state-of-the-art deep clustering methods.