Leiva-Murillo Jose Miguel, Artés-Rodríguez Antonio
Department of Signal Theory and Communications, Universidad Carlos III, Madrid 28911, Spain.
IEEE Trans Neural Netw. 2007 Sep;18(5):1433-41. doi: 10.1109/tnn.2007.891630.
In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.
在本文中，我们提出了一种用于分类中线性特征提取的新颖方案。该方法基于所提取特征与类别之间互信息（MI）的最大化。将每个特征对应的MI之和作为一种启发式方法，它近似于整个输出向量的MI。然后，提出了一种逐分量梯度上升方法来最大化MI，这类似于独立成分分析（ICA）中基于梯度的熵优化。仿真结果表明，该方法不仅在所有研究的情况下与现有的监督特征提取方法相比具有竞争力，而且当数据的类别之间具有强烈非线性边界时，它还显著优于这些方法。
