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The Infinite Mixture of Infinite Gaussian Mixtures
Halid Z Yerebakan · Bartek Rajwa · Murat Dundar

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

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 infinite Gaussian mixtures (I2GMM) for more flexible modeling of data sets with skewed and multi-modal cluster distributions. Instead of using a single Gaussian for each cluster as in the standard DPMG model, the generative model of I2GMM uses a single DPMG for each cluster. The individual DPMGs are linked together through centering of their base distributions at the atoms of a higher level DP prior. Inference is performed by a collapsed Gibbs sampler that also enables partial parallelization. Experimental results on several artificial and real-world data sets suggest the proposed I2GMM model can predict clusters more accurately than existing variational Bayes and Gibbs sampler versions of DPMG.

Author Information

Halid Z Yerebakan (IUPUI)
Bartek Rajwa (Purdue University)
Murat Dundar (IUPUI)

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