Jiang Qikun, Shi Jun
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3366-9. doi: 10.1109/EMBC.2014.6944344.
The neuroimaging data typically has extremely high dimensions. Therefore, dimensionality reduction is commonly used to extract discriminative features. Kernel entropy component analysis (KECA) is a newly developed data transformation method, where the key idea is to preserve the most estimated Renyi entropy of the input space data set via a kernel-based estimator. Despite its good performance, KECA still suffers from the problem of low computational efficiency for large-scale data. In this paper, we proposed a sparse KECA (SKECA) algorithm with the recursive divide-and-conquer based solution, and then applied it to perform dimensionality reduction of neuroimaging data for classification of the Alzheimer's disease (AD). We compared the SKECA with KECA, principal component analysis (PCA), kernel PCA (KPCA) and sparse KPCA. The experimental results indicate that the proposed SKECA has most superior performance to all other algorithms when extracting discriminative features from neuroimaging data for AD classification.
神经影像数据通常具有极高的维度。因此，降维常用于提取具有判别力的特征。核熵成分分析（KECA）是一种新开发的数据变换方法，其关键思想是通过基于核的估计器保留输入空间数据集的最大估计雷尼熵。尽管KECA性能良好，但对于大规模数据，它仍然存在计算效率低的问题。在本文中，我们提出了一种基于递归分治解决方案的稀疏KECA（SKECA）算法，然后将其应用于对阿尔茨海默病（AD）进行分类的神经影像数据降维。我们将SKECA与KECA、主成分分析（PCA）、核主成分分析（KPCA）和稀疏KPCA进行了比较。实验结果表明，在从神经影像数据中提取用于AD分类的判别特征时，所提出的SKECA比所有其他算法具有最优越的性能。
