摘要： 针对高维数据的维灾问题，采用核熵成分分析方法降维数据，并与主成分分析及核主成分分析方法进行对比。降维后的数据利用支持向量机算法进行分类，以验证算法有效性。实验结果表明，KECA在较低的维数时仍然能获得较好的分类精度，可以减少后续的处理复杂度和运行时间，适用于机器学习、模式识别等领域。

关键词: 降维, 核熵成分分析, 核主成分分析, 支持向量机

Abstract: Aiming at the curse of dimensionality, the kernel entropy component analysis(KECA) is used to reduce the dimension of data, which is compared with Principal Component Analysis(PCA) and Kernel PCA(KPCA). The low dimensional data after dimension reduction are classified by Support Vector Machine(SVM) algorithm to compare the accuracy. Experimental results indicate that high classification accuracy can be obtained at low dimension number with KECA, which reduces the processing complexity and running time. It suggests that KECA-based dimension reduction algorithm has the feasibility to be applied in the fields of machine learning, pattern recognition, etc.

Key words: dimension reduction, Kernel Entropy Component Analysis(KECA), Kernel Principal Component Analysis(KPCA), Support Vector Machine(SVM)

中图分类号: 

• TN911.72