Graph-modified neighborhood preserving embedding based on feature fusion

摘要：

Neighborhood preserving embedding (NPE) is a typical graph-based dimensionality reduction algorithm, which has been successfully applied in many practical problems such as face representation and recognition. NPE depends mainly on its underlying graph matrix which characters the local neighborhood reconstruction relationship between data points. However, the graph constructed in NPE merely utilizes the local structure information in the original data space which can not accurately reveal the local neighborhood structure of the data due to its high-dimensionality. To attack this problem, we propose a novel algorithm called graph-modified neighborhood embedding (GmNPE) based on feature fusion in this paper. The main idea is to utilize different local structure information in different lowdimensional feature space to construct the graph matrix. Experiments on JAFFE and Cohn-Kanade databases show the effectiveness of the GmNPE algorithm.

关键词： Dimensionality reduction Graph-modified neighborhood preserving embedding Feature fusion Facial expression recognition

作者: Song Guo Qiuqi Ruan

作者单位: Institute of Information Science, Beijing Jiaotong University, Beijing 100044, P.R.China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 1297-1300

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）