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Clustering Singular and Non-Singular Covariance Matrices for Classification

Abstract Number:

3080 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Andrew Simpson (1), Semhar Michael (2)

Institutions:

(1) N/A, N/A, (2) South Dakota State University, N/A

Co-Author:

Semhar Michael  
South Dakota State University

First Author:

Andrew Simpson  
N/A

Presenting Author:

Andrew Simpson  
N/A

Abstract Text:

In classification problems when working in high dimensions with a large number of classes and few observations per class, linear discriminant analysis (LDA) requires the strong assumptions of a shared covariance matrix between all classes and quadratic discriminant analysis leads to singular or unstable covariance matrix estimates. Both of these can lead to lower than desired classification performance. We introduce a novel, model-based clustering method which can relax the shared covariance assumptions of LDA by clustering sample covariance matrices, either singular or non-singular. This will lead to covariance matrix estimates which are pooled within each cluster. We show using simulated and real data that our method for classification tends to yield better discrimination compared to other methods.

Keywords:

Finite Mixture Models|EM-algorithm|Model Based Clustering|Classification|Singular Covariance Matrices|Pattern Recognition

Sponsors:

Section on Statistical Computing

Tracks:

Computationally Intensive Methods

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