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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Input variable selection: mutual information and linear mixing measures
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Input variable selection: mutual information and linear mixing measures

机译：输入变量选择：互信息和线性混合度量

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摘要

Determining the most appropriate inputs to a model has a significant impact on the performance of the model and associated algorithms for classification, prediction, and data analysis. Previously, we proposed an algorithm ICAIVS which utilizes independent component analysis (ICA) as a preprocessing stage to overcome issues of dependencies between inputs, before the data being passed through to an input variable selection (IVS) stage. While we demonstrated previously with artificial data that ICA can prevent an overestimation of necessary input variables, we show here that mixing between input variables is common in real-world data sets so that ICA preprocessing is useful in practice. This experimental test is based on new measures introduced in this paper. Furthermore, we extend the implementation of our variable selection scheme to a statistical dependency test based on mutual information and test several algorithms on Gaussian and sub-Gaussian signals. Specifically, we propose a novel method of quantifying linear dependencies using ICA estimates of mixing matrices with a new linear mixing measure (LMM).
机译：确定模型的最适当输入对模型的性能以及用于分类，预测和数据分析的相关算法的性能具有重大影响。先前，我们提出了一种算法ICAIVS，该算法利用独立成分分析（ICA）作为预处理阶段来克服输入之间的依存关系问题，然后再将数据传递到输入变量选择（IVS）阶段。尽管我们之前用人工数据证明了ICA可以防止高估必要的输入变量，但在这里我们表明，输入变量之间的混合在现实世界的数据集中很常见，因此ICA预处理在实践中很有用。此实验测试基于本文介绍的新措施。此外，我们将变量选择方案的实现扩展到基于互信息的统计相关性测试，并测试了针对高斯和次高斯信号的几种算法。具体而言，我们提出了一种使用ICA混合矩阵估计和新的线性混合度量（LMM）来量化线性相关性的新颖方法。

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