A Mixture of Variational Autoencoders for Deep Clustering
In this study we propose a deep clustering algorithm that utilizes variational auto encoder (VAE) framework with a multi encoder-decoder neural architecture. This setup enforces a complementary structure that guides the learned latent representations towards a more meaningful arrangement in space. It differs from previous VAE-based clustering algorithms by the employment of a new generative model that uses multiple encoder-decoders. We show that this modelling 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.
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