title: Dimension selection for feature selection and dimension reduction with principal and independent component analysis.
abstract: This letter is concerned with the problem of selecting the best or most informative dimension for dimension reduction and feature extraction in high-dimensional data. The dimension of the data is reduced by principal component analysis; subsequent application of independent component analysis to the principal component scores determines the most nongaussian directions in the lower-dimensional space. A criterion for choosing the optimal dimension based on bias-adjusted skewness and kurtosis is proposed. This new dimension selector is applied to real data sets and compared to existing methods. Simulation studies for a range of densities show that the proposed method performs well and is more appropriate for nongaussian data than existing methods.
authors.affiliation: Department of Statistics, School of Mathematics, University of New South Wales, Sydney, NSW 2052 Australia. inge@maths.unsw.edu.au
authors.forename: Inge
authors.initials: I
authors.lastname: Koch
authors.forename: Kanta
authors.initials: K
authors.lastname: Naito
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D025341:Principal Component Analysis