Title

Session 4 - Advances in Probabilistic Modeling for Machine Learning: Gaussian Mixture Modeling and Model-based Clustering under Measurement Inconsistency

Presenter Information/ Coauthors Information

Rong Zheng, Western Illinois University

Presentation Type

Invited

Abstract

Finite mixtures present a powerful tool for modeling complex heterogeneous data. One of their most important applications is model-based clustering. It assumes that each data group can be reasonably described by one of mixture model components. This establishes a one-to-one relationship between mixture components and clusters. In some cases, however, this relationship can be broken due to the presence of observations from the same class recorded in different ways. This effect can occur because of recording inconsistencies due to the use of different scales, operator errors, or simply various recording styles. The idea presented in this paper aims to alleviate this issue through modifications incorporated into mixture models. While the proposed methodology is applicable to a broad class of mixture models, in this paper it is illustrated on Gaussian mixtures. Several simulation studies and an application to a real-life data set are considered, yielding promising results.

Start Date

2-11-2020 9:30 AM

End Date

2-11-2020 10:30 AM

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Feb 11th, 9:30 AM Feb 11th, 10:30 AM

Session 4 - Advances in Probabilistic Modeling for Machine Learning: Gaussian Mixture Modeling and Model-based Clustering under Measurement Inconsistency

Campanile & Hobo Day Gallery (A & B)

Finite mixtures present a powerful tool for modeling complex heterogeneous data. One of their most important applications is model-based clustering. It assumes that each data group can be reasonably described by one of mixture model components. This establishes a one-to-one relationship between mixture components and clusters. In some cases, however, this relationship can be broken due to the presence of observations from the same class recorded in different ways. This effect can occur because of recording inconsistencies due to the use of different scales, operator errors, or simply various recording styles. The idea presented in this paper aims to alleviate this issue through modifications incorporated into mixture models. While the proposed methodology is applicable to a broad class of mixture models, in this paper it is illustrated on Gaussian mixtures. Several simulation studies and an application to a real-life data set are considered, yielding promising results.