Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering

Abstract: To avoid the curse of dimensionality, a common approach to clustering high-dimensional data is to first project the data into a space of reduced dimension, and then cluster the projected data. Although effective, this two-stage approach prevents joint optimization of the dimensionality-reduction and clustering models, and obscures how well the complete model describes the data. Here, we show how a family of such two-stage models can be combined into a single, hierarchical model that we call a hierarchical mixture of Gaussians (HMoG). An HMoG simultaneously captures both dimensionality-reduction and clustering, and its performance is quantified in closed-form by the likelihood function. By formulating and extending existing models with exponential family theory, we show how to maximize the likelihood of HMoGs with expectation-maximization. We apply HMoGs to synthetic data and RNA sequencing data, and demonstrate how they exceed the limitations of two-stage models. Ultimately, HMoGs are a rigorous generalization of a common statistical framework, and provide researchers with a method to improve model performance when clustering high-dimensional data.