Journal
BIOMETRICS
Volume 64, Issue 2, Pages 440-448Publisher
WILEY
DOI: 10.1111/j.1541-0420.2007.00922.x
Keywords
EM algorithm; high-dimension low sample size; microarray; model-based clustering; regularization; variable selection
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Variable selection in high-dimensional clustering analysis is an important yet challenging problem. In this article, we propose two methods that simultaneously separate data points into similar clusters and select informative variables that contribute to the clustering. Our methods are in the framework of penalized model-based clustering. Unlike the classical L-1-norm penalization, the penalty terms that we propose make use of the fact that parameters belonging to one variable should be treated as a natural group. Numerical results indicate that the two new methods tend to remove noninformative variables more effectively and provide better clustering results than the L-1-norm approach.
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