  In this paper we propose a Deep Autoencoder MIxture Clustering (DAMIC)
algorithm based on a mixture of deep autoencoders where each cluster is
represented by an autoencoder. A clustering network transforms the data into
another space and then selects one of the clusters. Next, the autoencoder
associated with this cluster is used to reconstruct the data-point. The
clustering algorithm jointly learns the nonlinear data representation and the
set of autoencoders. The optimal clustering is found by minimizing the
reconstruction loss of the mixture of autoencoder network. Unlike other deep
clustering algorithms, no regularization term is needed to avoid data
collapsing to a single point. Our experimental evaluations on image and text
corpora show significant improvement over state-of-the-art methods.
