Tensor Rank One Discriminant Locally Linear Embedding for Facial Expression Classification

摘要：

In this paper we propose the Tensor Rank one Discriminant Locally Linear Embedding algorithm (TR1DLLE), which accept tensors as input for classification. TR1DLLE integrates the tensor rank one Analysis (TR1A) and a recently proposed graph embedding algorithm Discriminant Locally Linear Embedding (DLLE). The merits of TR1DLLE include: (1) representing data in their native structure without losing spatial locality information; (2) avoiding the curse of dimensionality and small sample size problems; (4) inheriting the excellent characters of DLLE about intraclass manifold preservation and interclass discrimination; (5) having better learning capacity especially when the size of the training sample is small; (6) converge well. In the experiments, we apply TR1DLLE to the facial expressions classification and compared it with the former related algorithms.

关键词： Dimension reduction Tensor Rank One Analysis(TR1A) Tensor Rank One Discriminant Locally linear Embedding (TR1DLLE) Facial expression recognition

作者: Shuai Liu Qiuqi Ruan

作者单位: Institute of Information Science Beijing Jiaotong University Beijing, 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: 1410-1413

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）