Manifold Kernel Sparse Representation of Symmetric Positive-Definite Matrices and Its Applications
Manifold Kernel Sparse Representation of Symmetric Positive-Definite Matrices and Its Applications
复制标题
对称正定矩阵的流形核稀疏表示及其应用
DOI：
10.1109/tip.2015.2451953
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发表时间：
2015-07
影响因子：
10.6
通讯作者：
Yuan Junsong
中科院分区：
文献类型：
--
作者：
Wu Yuwei;Jia Yunde;Li Peihua;Zhang Jian;Yuan Junsong
The symmetric positive-definite (SPD) matrix, as a connected Riemannian manifold, has become increasingly popular for encoding image information. Most existing sparse models are still primarily developed in the Euclidean space. They do not consider the non-linear geometrical structure of the data space, and thus are not directly applicable to the Riemannian manifold. In this paper, we propose a novel sparse representation method of SPD matrices in the data-dependent manifold kernel space. The graph Laplacian is incorporated into the kernel space to better reflect the underlying geometry of SPD matrices. Under the proposed framework, we design two different positive definite kernel functions that can be readily transformed to the corresponding manifold kernels. The sparse representation obtained has more discriminating power. Extensive experimental results demonstrate good performance of manifold kernel sparse codes in image classification, face recognition, and visual tracking.
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DOI：
10.1609/aaai.v29i1.9595
发表时间：
2015-01
期刊：
--
影响因子：
--
作者：
Shengping Zhang;S. Kasiviswanathan;P. Yuen;Mehrtash Harandi
通讯作者：
Shengping Zhang;S. Kasiviswanathan;P. Yuen;Mehrtash Harandi
DOI：
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发表时间：
2009
期刊：
--
影响因子：
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作者：
Xue Mei;Haibin Ling
通讯作者：
Xue Mei;Haibin Ling
DOI：
--
发表时间：
1984
期刊：
--
影响因子：
--
作者：
C. Berg;J. Christensen;P. Ressel
通讯作者：
C. Berg;J. Christensen;P. Ressel
DOI：
10.1109/cvpr.2009.5206806
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
H. E. Çetingül;R. Vidal
通讯作者：
H. E. Çetingül;R. Vidal
DOI：
10.1109/cvpr.2010.5539917
发表时间：
2010-06
期刊：
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
Nianhua Xie;Haibin Ling;Weiming Hu;Xiaoqin Zhang
通讯作者：
Nianhua Xie;Haibin Ling;Weiming Hu;Xiaoqin Zhang