期刊
PATTERN RECOGNITION
卷 44, 期 7, 页码 1497-1513出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2010.12.024
关键词
Dimensionality reduction; Generalized tensor subspace model; Orthonormal basis tensor; Orthogonal Tensor Neighborhood; Preserving Embedding (OTNPE); Facial expression recognition
资金
- National Natural Science Foundation of China [60973060]
- Doctorial Foundation of Ministry of Education of China [200800040008]
- Educational Committee of Beijing [YB20081000401]
In this paper a generalized tensor subspace model is concluded from the existing tensor dimensionality reduction algorithms. With this model, we investigate the orthogonality cif the bases of the high-order tensor subspace, and propose the Orthogonal Tensor Neighborhood Preserving Embedding (OTNPE) algorithm. We evaluate the algorithm by applying it to facial expression recognition, where both the 2nd-order gray-level raw pixels and the encoded 3rd-order tensor-formed Gabor features of facial expression images are utilized. The experiments show the excellent performance of our algorithm for the dimensionality reduction of the tensor-formed data especially when :hey lie on some smooth and compact manifold embedded in the high dimensional tensor space. (c) 2011 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据