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Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method to solve the mode mixing problem caused by empirical mode decomposition (EMD), which is a significant step of Hilbert-Huang Transform (HHT). In this paper, a novel fast EEMD preferences algorithm called Quasi-Gradient Search (QGS) is proposed. For a given ensemble number, we first apply Nonlinear Correlation...
Mutual information (MI) has obvious potential for feature selection, but this has not been fully exploited in the past. In order to make numerical computation easier and more accurate, the MI of the whole multi-dimensional data can be decomposed into an amount of one-dimensional MI and one-dimensional conditional MI components. This paper reveals that using one-dimensional MI components to replace...
Many feature selection methods have been proposed in recent years, but there is little work concerning the evaluation of the performances with respect to different feature selection methods especially when the ground truth map is unavailable. In this paper, a new method called quantitative multivariate correlation analysis (QMCA) is proposed, which provides a quantitative measure of the useful information...
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