In this paper we propose a Deep Autoencoder Mixture Clustering (DAMIC) algorithm. It is 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.
We propose a deep clustering method where instead of a centroid each cluster is represented by an autoencoder Presents deep clustering based on a mixture of autoencoders, where data points are allocated to a cluster based the representation error if the autoencoder network were used to represent it. A deep clustering approach that uses an autoencoder framework to learn a low-dimensional embedding of the data simultaneously while clustering data using a deep neural network. A deep clustering method which represents each cluster with different auto-encoders, works in an end-to-end manner, and also can be used to cluster new incoming data without redoing the whole clustering procedure.