Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering
Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering
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DOI：
10.26636/jtit.2015.2.782
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发表时间：
2015-06
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通讯作者：
K. Slot;Krzysztof Adamiak;P. Duch;Dominik Żurek
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文献类型：
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作者：
K. Slot;Krzysztof Adamiak;P. Duch;Dominik Żurek
本文研究了通过特征选择来获得特征空间的方法。对输入数据样本的核主成分分析(KPCA)的结果进行选择。介绍了驱动特征选择过程的几个准则，并对它们的性能进行了评估，并与基于Fisher线性判别准则的kPCA和最具表现力特征重排序相结合的参考方法进行了比较。结果表明，所提出的一些改进可以产生具有明显更好的(大约4%的)类别区分性质的特征空间。
The presented paper is concerned with feature space derivation through feature selection. The selection is performed on results of kernel Principal Component Analysis (kPCA) of input data samples. Several criteria that drive feature selection process are introduced and their performance is assessed and compared against the reference approach, which is a combination of kPCA and most expressive feature reordering based on the Fisher linear discriminant criterion. It has been shown that some of the proposed modifications result in generating feature spaces with noticeably better (at the level of approximately 4%) class discrimination properties.