基于最大判别熵的有监督独立分量分析方法

独立分量分析(indepentlent component analysis，ICA)是目前非常活跃的一个研究领域，在盲源分离、信号处理等方面有着广泛的应用．特别是在特征提取方面，由于其处理非高斯分布的数据的能力，引起了广泛关注，取得了很好的效果．但是传统的独立分量分析方法的思想都是通过定义一个衡量分量独立性的目标函数来求解问题，在应用到特征提取方面时，没有考虑到提取的独立分量对于识别和分类问题的重要性．为了克服传统ICA算法的不足，从信息论角度出发，选择判别熵作为衡量类别之问差异的度量，提出了基于最大判别熵的有监督独立分量分析方法(SICA-MJE)，并在人脸识别和虹膜识别应用中进行了验证，取得了很好的实验结果。

Supervised Independent Component Analysis by Maximizing J-Divergence Entropy

Independent component analysis (ICA) is one of the most exciting topics in the fields of neural computation, advanced statistics, and signal processing, which finds independent components from observing multidimensional data based on higher order statistics. The theory of independent component analysis is traditionally associated with the blind source separation (BSS) . Since the recent increase of interest in ICA, it has been clear that this principle has a lot of other interesting applications, especially feature extraction. But traditional independent component analysis mainly aims at BSS and is not suitable for recognition and classification due to ignorance of the contribution of independent components to recognition performance. In order to overcome this problem, a new supervised ICA algorithm based on J-divergence entropy is proposed, which can measure the difference of classes. Experiment results of face and iris recognition show that the algorithm improves the performance efficiently.