Principal Composite Kernel Feature Analysis: Data-Dependent Kernel Approach
Principal Composite Kernel Feature Analysis: Data-Dependent Kernel Approach
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DOI：
10.1109/tkde.2012.110
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发表时间：
2013-08
影响因子：
8.9
通讯作者：
Yuichi Motai;H. Yoshida
中科院分区：
文献类型：
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作者：
Yuichi Motai;H. Yoshida
针对计算机辅助诊断（CAD）中医学图像数据集（MIDS）的非线性特征，提出了主复合核特征分析（PC-KFA）。提出的算法PC-KFA扩展了现有的核特征分析（KFA）的研究，它提取显着特征的未分类模式的样本使用的核方法。本文中用于PC-KFA的主复合过程已被应用于内核主成分分析[34]和我们先前开发的加速内核特征分析[20]。与其他基于核的特征选择算法不同，PC-KFA通过最大化非线性变换样本的方差条件迭代地构造高维特征空间的线性子空间，我们称之为数据依赖核方法。该方法首先通过主成分分析选择核子空间，然后通过有效的组合表示方法得到复合核子空间，用于进一步的重构和分类。基于癌症CAD数据的几个MID特征空间的数值实验表明，PC-KFA生成高效和有效的特征表示，并已产生了更好的分类性能，建议的复合核子空间使用一个简单的模式分类器。
Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image data sets (MIDS) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis [34] and to our previously developed accelerated kernel feature analysis [20]. Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis, and then be processed for composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates efficient and an effective feature representation, and has yielded a better classification performance for the proposed composite kernel subspace using a simple pattern classifier.