Dimension Selection for Feature Selection and Dimension Reduction with Principal and Independent Component Analysis
Dimension Selection for Feature Selection and Dimension Reduction with Principal and Independent Component Analysis
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DOI：
10.1162/neco.2007.19.2.513
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发表时间：
2007-02
影响因子：
2.9
通讯作者：
Inge Koch;K. Naito
中科院分区：
文献类型：
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作者：
Inge Koch;K. Naito
这封信关注的是在高维数据中选择最佳或最具信息量的维度进行降维和特征提取的问题。通过主成分分析降低数据的维度;随后将独立成分分析应用于主成分得分，确定低维空间中的最非高斯方向。提出了一种基于偏差调整偏度和峰度的最优维数选择准则。这种新的维度选择器被应用到真实的数据集，并与现有的方法进行比较。一系列密度的模拟研究表明，所提出的方法表现良好，是更适合于非高斯数据比现有的方法。
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.