288 pp. per issue
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Neural Computation

February 2007, Vol. 19, No. 2, Pages 513-545
(doi: 10.1162/neco.2007.19.2.513)
© 2007 Massachusetts Institute of Technology
Dimension Selection for Feature Selection and Dimension Reduction with Principal and Independent Component Analysis
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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.