Abstract
Principal components are often used for reducing dimensions in multivariate data, but they frequently fail to provide useful results and their interpretation is rather difficult. In this article, the use of entropy optimization principles for dimensional reduction in multivariate data is proposed. Under the assumptions of multivariate normality, a four-step procedure is developed for selecting principal variables and hence discarding redundant variables. For comparative performance of the information theoretic procedure, we use simulated data with known dimensionality. Principal variables of cluster bean (Guar) are identified by applying this procedure to a real data set generated in a plant breeding experiment.
Mathematics Subject Classification:
Acknowledgments
The authors would like to express their appreciation to two anonymous referees for their excellent suggestions in strengthening this manuscript. We also appreciate the patience Professor N. Balakrishnan, Editor-in-Chief, and Ms. Debbie Iscoe, Editorial Assistant, have had with us.
Notes
LGD: Least generalized dependence
δ*: Normalized measure of generalized dependence