K-Autoencoders Deep Clustering

Yaniv Opochinsky, Shlomo E. Chazan, Sharon Gannot, Jacob Goldberger

Abstract: In this study we propose a deep clustering algorithm that extends the k-means algorithm. Each cluster is represented by an autoencoder instead of a single centroid vector. Each data point is associated with the autoencoder which yields the minimal reconstruction error. The optimal clustering is found by learning a set of autoencoders that minimize the global reconstruction mean-square error loss. The network architecture is a simplified version of a previous method that is based on mixture-of-experts. The proposed method is evaluated on standard image corpora and performs on par with state-of-the-art methods which are based on much more complicated network architectures.