Sparse alignment for robust tensor learning

Zhihui Lai, Wai Keung Wong, Yong Xu, Cairong Zhao, Mingming Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

58 Citations (Scopus)

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, L1- and L2-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.
Original languageEnglish
Article number6710130
Pages (from-to)1779-1792
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number10
DOIs
Publication statusPublished - 1 Oct 2014

Keywords

  • Feature extraction
  • local alignment
  • manifold learning
  • sparse representation
  • tensor learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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