Deep Unsupervised Clustering Using Mixture of Autoencoders
This addresses the fundamental problem of clustering for machine learning applications, but it is incremental as it builds on existing autoencoder and mixture model approaches.
The paper tackles unsupervised clustering by proposing a mixture of autoencoders model that jointly assigns data to clusters and learns underlying manifolds, achieving competitive results on benchmark datasets.
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.