Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3857–3870 | Cite as

A multilinear unsupervised discriminant projections method for feature extraction

  • Haiyan Chen
  • Chengshan Qian
  • Hao Zheng
  • Huan Wang


Despite considering the distribution information of data, unsupervised discriminant projection (UDP) ignores the space structure information of data for high order tensor objects. To address these problems, many tensor methods are developed for charactering the space structure information. Albeit effective, these methods ignore the local manifold structure of the samples, and thus achieve sub-optimal performance. In this paper, we formulate UDP in a high order tensor space and develop a Multilinear UDP (MUDP) for feature extraction on tensor objects. MUDP inherits the merits of UDP and Tensor based methods. The experiments tell that MUDP is an efficient and effective method and works well.


UDP Tensor Multilinear Feature extraction Face recognition 



This work is partly supported by Natural Science Foundation of China (61603190, 31671006) and the Natural Science Foundation of Jiangsu Province (No.BK20140638, BK2012437).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Haiyan Chen
    • 1
    • 2
  • Chengshan Qian
    • 3
  • Hao Zheng
    • 2
  • Huan Wang
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.Key Laboratory of Trusted Cloud Computing and Big Data AnalysisNanjing Xiaozhuang UniversityNanjingChina
  3. 3.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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