Classifying with Gaussian Mixtures and Clusters

The Infinite Mixture of Infinite Gaussian Mixtures

Dirichlet process mixture of Gaussians (DPMG) has been used in the literature for clustering and density estimation problems. However, many real-world data exhibit cluster distributions that cannot be captured by a single Gaussian. Modeling such data sets by DPMG creates several extraneous clusters even when clusters are relatively well-defined. Herein, we present the infinite mixture of infini...

The Infinite Mixture of Infinite Gaussian Mixtures

Dirichlet process mixture of Gaussians (DPMG) has been used in the literature for clustering and density estimation problems. However, many real-world data exhibit cluster distributions that cannot be captured by a single Gaussian. Modeling such data sets by DPMG creates several extraneous clusters even when clusters are relatively well-defined. Herein, we present the infinite mixture of infini...

The Infinite Mixture of Infinite Gaussian Mixtures for Clustering Data Sets with Multi-mode and Rare Clusters Supplementary Material

Scalable model-based cluster analysis using clustering features

We present two scalable model-based clustering systems based on a Gaussian mixture model with independent attributes within clusters. They first summarize data into sub-clusters, and then generate Gaussian mixtures from their clustering features using a new algorithm — EMACF. EMACF approximates the aggregate behavior of each sub-cluster of data items in the Gaussian mixture model. It provably c...

Dictionary-based decomposition of linear mixtures of Gaussian processes

We consider the problem of detecting and classifying an unknown number of multiple simultaneous Gaussian processes with unknown variances given a nite length observation of their sum and a dictionary of candidate models for the signals. The optimal minimum description length (MDL) detector is presented. Asymptotic and quadratic approximations of the MDL criterion are derived, and reg-ularizatio...