Title: Deep Unsupervised Clustering Using Mixture of Autoencoders,
Abstract: Unsupervised clustering is one of the most fundamental challenges in machine
learning. A popular hypothesis is that data are generated from a union of
low-dimensional nonlinear manifolds; thus an approach to clustering is
identifying and separating these manifolds. In this paper, we present a novel
approach to solve this problem by using a mixture of autoencoders. Our model
consists of two parts: 1) a collection of autoencoders where each autoencoder
learns the underlying manifold of a group of similar objects, and 2) a mixture
assignment neural network, which takes the concatenated latent vectors from the
autoencoders as input and infers the distribution over clusters. By jointly
optimizing the two parts, we simultaneously assign data to clusters and learn
the underlying manifolds of each cluster.