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Activity Number: 360 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #313211
Title: The Cauchy-Net Mixture Model for Clustering with Anomalous Data
Author(s): Matt Slifko* and Scotland Leman and David Bieri
Companies: High Point University and Virginia Tech and Virginia Tech
Keywords: Bayesian Nonparametrics; Dirichlet Process Mixture; Clustering; Anomaly Detection
Abstract:

The unprecedented amount of data at our fingertips offers a potential wealth of knowledge but also brings about concerns regarding ethical collection and usage. One such concern stems from the potential real-life consequences of injecting anomalous data into our models. We develop the Cauchy-Net Mixture Model (CNMM), which is a framework that allows for simultaneously clustering observations, making predictions, and identifying anomalies. The CNMM extends the flexibility of a Dirichlet Process Mixture Model (DPMM) by creating a mixture of a DPMM with an additional Cauchy distributed component, which we refer to as the Cauchy-Net (CN). The intuition is to leverage the heavy tails of the CN for capturing observations that do not fit into the well-defined clusters in order to remove their influence on cluster formation and prediction. We demonstrate the usefulness of the CNMM in a variety of experimental situations and apply the model for predicting housing prices in Fairfax County, Virginia.


Authors who are presenting talks have a * after their name.

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