Deep Unsupervised Clustering with Clustered Generator Model

However, unsupervised clustering remains one of the most fundamental challenges in machine learning because of high dimensionality of data and high complexities of their hidden structures. Long-established approaches for unsupervised clustering including K-means [15] and Gaussian Mixture Model (GMM) [3] are still the building blocks for numerous applications due to their efficiency and simplicity. However, their distance metrics are limited to data space, making them ineffective for high-dimensional data such as images. Therefore, considerable efforts have been put into obtaining a good feature embedding of data, usually of low dimensionality, for effective clustering [37]. However, the representation obtained by standalone data embedding typically can-Tian Han is the corresponding author not capture the latent structure and variation of the observed data which may be ineffective for clustering. We believe the good representation for clustering should also be able to compactly represent the observed data distribution to encode all necessary characteristics of the observation. Deep generative models (a.k.a the generator models) have shown great promise in learning latent representations for high-dimensional signals such as images and videos [32, 24, 11]. Generator models parameterized by deep neural networks specify a nonlinear mapping from latent variables to observed data.