A METHOD FOR IMAGE CLASSIFICATION BASED ON KERNEL PCA

摘要：

This paper adopts unsupervised on-line shape learning for image analysis tasks, removing the requirement for a pre-defined set of templates and allowing the system to handle novel objects. This learning approach was chosen for its simplicity and extensibility. The results show that the size and shape features are sufficient for accurate object classification. We briefly focused on how to use and work with the kernel-based algorithm in radial basis function neural networks. Kernel PCA, as an unsupervised learning method, is a nonlinear extension of PCA for finding projections that give useful nonlinear descriptors of the data.

关键词： Cluster Kernel PCA RBF neural networks Unsupervised learning

作者: SU YAN JIU-FEN ZHAO JIU-LING ZHAO QING-ZHEN LI

作者单位: Tsinghua University The Second Artillery Engineering Institute

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 718-722

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）