Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35916
Title: Sparse alignment for robust tensor learning
Authors: Lai, ZH
Wong, WK 
Xu, Y
Zhao, CR
Sun, MM
Keywords: Feature extraction
Local alignment
Manifold learning
Sparse representation
Tensor learning
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2014, v. 25, no. 10, p. 1779-1792 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: 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.
URI: http://hdl.handle.net/10397/35916
ISSN: 2162-237X (print)
2162-2388 (online)
DOI: 10.1109/TNNLS.2013.2295717
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

14
Citations as of Jan 6, 2017

WEB OF SCIENCETM
Citations

13
Last Week
0
Last month
Citations as of Jan 15, 2017

Page view(s)

11
Last Week
0
Last month
Checked on Jan 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.