期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 25, 期 10, 页码 1779-1792出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2013.2295717
关键词
Feature extraction; local alignment; manifold learning; sparse representation; tensor learning
类别
资金
- Natural Science Foundation of China [61203376, 61375012, 61203247, 61005005, 61071179, 61125305, 61170077, 61362031, 61332011, 61370163, 61263032]
- General Research Fund of Research Grants Council of Hong Kong [531708]
- China Postdoctoral Science Foundation [2012M510958, 2013T60370]
- Guangdong Natural Science Foundation [S2012040007289]
- Shenzhen Municipal Science and Technology Innovation Council [JC201005260122A, JCYJ20120613153352732, JCYJ20120613134843060, JCYJ20130329152024199]
Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L-1- and L-2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.
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