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https://hdl.handle.net/2440/55367
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Type: | Journal article |
Title: | Dimension Selection for Feature Selection and Dimension Reduction with Principal and Independent Component Analysis |
Author: | Koch, I. Naito, K. |
Citation: | Neural Computation, 2007; 19(2):513-545 |
Publisher: | M I T Press |
Issue Date: | 2007 |
ISSN: | 0899-7667 1530-888X |
Statement of Responsibility: | Inge Koch and Kanta Naito |
Abstract: | <jats:p> 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. </jats:p> |
DOI: | 10.1162/neco.2007.19.2.513 |
Published version: | http://dx.doi.org/10.1162/neco.2007.19.2.513 |
Appears in Collections: | Aurora harvest Mathematical Sciences publications |
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