Model-based principal components of covariance matrices

2010 ◽  
Vol 63 (1) ◽  
pp. 113-137 ◽  
Author(s):  
Robert J. Boik ◽  
Kamolchanok Panishkan ◽  
Scott K. Hyde
Keyword(s):  
Principal Components ◽  
Covariance Matrices ◽  
Model Based

scholarly journals On the principal components of sample covariance matrices

2015 ◽  
Vol 164 (1-2) ◽  
pp. 459-552 ◽  
Author(s):  
Alex Bloemendal ◽  
Antti Knowles ◽  
Horng-Tzer Yau ◽  
Jun Yin
Keyword(s):  
Principal Components ◽  
Covariance Matrices ◽  
Sample Covariance Matrices ◽  
Sample Covariance

scholarly journals Model-based clustering with sparse covariance matrices

2018 ◽  
Vol 29 (4) ◽  
pp. 791-819 ◽  
Author(s):  
Michael Fop ◽  
Thomas Brendan Murphy ◽  
Luca Scrucca
Keyword(s):  
Covariance Matrices ◽  
Model Based Clustering ◽  
Model Based

Robust Clustering Method in the Presence of Scattered Observations

2016 ◽  
Vol 28 (6) ◽  
pp. 1141-1162
Author(s):  
Akifumi Notsu ◽  
Shinto Eguchi
Keyword(s):  
Data Analysis ◽  
Degrees Of Freedom ◽  
Covariance Matrices ◽  
The Other ◽  
Proper Solution ◽  
Clustering Method ◽  
Robust Clustering ◽  
Standard Methods ◽  
Model Based Clustering ◽  
Model Based

Contamination of scattered observations, which are either featureless or unlike the other observations, frequently degrades the performance of standard methods such as K-means and model-based clustering. In this letter, we propose a robust clustering method in the presence of scattered observations called Gamma-clust. Gamma-clust is based on a robust estimation for cluster centers using gamma-divergence. It provides a proper solution for clustering in which the distributions for clustered data are nonnormal, such as t-distributions with different variance-covariance matrices and degrees of freedom. As demonstrated in a simulation study and data analysis, Gamma-clust is more flexible and provides superior results compared to the robustified K-means and model-based clustering.

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