Online Classification Framework for Data Stream Based on Incremental Kernel Principal Component Analysis
-
摘要: 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), 该算法在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在线分类框架: IKOCFrame (Online classification frame based on 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IKOCFrame相对于同类基于成分分析的分类算法的优越性.Abstract: 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.
点击查看大图
计量
- 文章访问数: 1846
- HTML全文浏览量: 34
- PDF下载量: 1221
- 被引次数: 0