核主成分分析(Kernel principal component analysis,KPCA)是一种非线性降维工具,在降低数据流分类处理量方面发挥着积极作用.然而,由于复杂性太高,导致KPCA的降维能力有限.为此,本文给出了一种增量核主成分分析算法(Incremental KPCA for dimensionality-reduction,IKDR),该算法存每步迭代估计中只需线性内存开销,大大降低了复杂性.在IKDR的基础上,结合BP(Back propagation)神经网络提出了数据流存线分类框架:IKOCFrame(Online classification flame basedon IKDR).通过一系列真实和人工数据集上的实验,检验了IKDR算法的收敛性,并且验证了IKOOFrame相对于同类基于成分分析的分类算法的优越性.
Kernel principal component analysis (KPCA) has been suggested for various data stream classification tasks requiring a nonlinear transformation scheme to reduce dimensions. However, the dimensionality reduction ability is restricted because of its high complexity. Therefore this paper proposes an incremental kernel principal component analysis algorithm: IKDR, which iteratively estimates the kernel principal components with only linear order storage complexity per iteration. On the basis of IKDR, this paper proposes an online classification framework for data stream: IKOCFrame. Extensive experiments on real and artificial datasets validate the convergence of IKDR and confirm the superiority of IKOCFrame over other recent classification schemes based on component analysis.