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Image Set Representation with Robust Manifold Regularized Low-Rank Approximation

  • Bo Jiang
  • Yuan Zhang
  • Youxia Cao
  • Bin Luo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

Many problems in computer vision and machine learning area can be formulated as image set representation and learning. In real application, image set data often contains various kinds of noises or missing value corruptions, and are also usually sampled from nonlinear manifolds. These make the representation and learning process of image set data more challengeable. This paper proposes a robust manifold regularized low-rank approximation (MLRA) method for image set recovery and representation. MLRA provides an effective low-rank representation for image set whose elements are sampled from nonlinear manifolds. Comparing with original observed image set, MLRA of image set is generally noiseless and more regular, which can obviously encourage the robust learning and recognition process. We evaluate our method on several datasets to demonstrate the benefits of the method.

Keywords

Manifold learning Image set Representation Low-rank approximation 

Notes

Acknowledgement

This work is supported by National Natural Science Foundation of China (61602001, 61472002); Natural Science Foundation of Anhui Province (1708085QF139); Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2016A020).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyAnhui UniversityHefeiChina

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