摘要
基于核的主成分分析(KPCA)方法能提取数据的非线性特征,但特征提取的效率却与训练样本集合的容量成反比.文中提出一种特征提取的自适应核特征子空间方法来快速有效地提取特征.该方法和KPCA方法在理论分析框架上是一致的,但通过自适应的选取核子空间的张成向量,能在提高特征提取效率的同时不影响特征提取的精度.针对模拟数据和MNIST数据的实验结果表明文中方法优于经典KPCA方法和参考方法.
Kernel principal component analysis （KPCA） can extract nonlinear features of datasets. However, its efficiency is inversely proportional to the size of the training sample set. In this paper, an adaptive kernel feature subspace method is proposed to extract features efficiently. This method is methodologically consistent with KPCA, and it improves the efficiency by adaptively selecting the spanning vectors of the KPCA without losing accuracy. Experimental results on two-dimensional data and MNIST datasets show that the proposed method is better than the one associated with KPCA and reference methods.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2013年第4期392-401,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.10926197
60972150)
中国留学基金委研究生项目(No.2011629111)资助
关键词
核主成分分析(KPCA)
特征提取
核子空间
张成向量
Kernel Principal Component Analysis （KPCA）, Feature Extraction, Kernel FeatureSubspace, Spanning Vector
