Dynamic Laplacian Principle Component Analysis On Objective Space

摘要：

Laplacian PCA tries to maximize the intra-class covariance across all samples while preserving the local manifold information.However,the static geometry center is incompetent to express the data’s manifold structure,and easily influenced by the unique noise point.Moreover,Laplacian PCA also neglects the upgraded information in the objective space.In this paper,we propose the Dynamic Laplacian PCA method,which introduces the gradient based Laplacian center to precisely illustrate the local manifold,besides,iterative Laplacian PCA is employed to optimize the feature vectors in the objective space.The experimental results on a face database and a virtual database show the promise of our method.

作者: Shaoe Xue Shuqin Rao Wenxin Yang Jina Wang Jian Yin

作者单位: Department of Computer Science Sun Yat-Sen University,Guangzhou,China,510275

会议类型: 国际会议

会议名称: 第一届智能网络与智能系统国际会议(ICINIS 2008)(The First International Conference on Intelligent Networks and Intelligent Systems)

会议地点: 武汉

会议语种:英文

在线出版日期: 2008-11-01（万方平台首次上网日期，不代表论文的发表时间）