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Signal, Image and Video Processing

, Volume 13, Issue 7, pp 1387–1394 | Cite as

Classification via semi-supervised multi-random subspace sparse representation

  • Zhuang Zhao
  • Lianfa Bai
  • Yi Zhang
  • Jing HanEmail author
Original Paper
  • 79 Downloads

Abstract

In this paper, we combine the random subspace and multi-view together and obtain a novel approach named semi-supervised multi-random subspace sparse representation (SSM-RSSR). In the proposed SSM-RSSR, firstly, we use subspace sparse representation to obtain the graph to characterize the distribution of samples in each subspace. Then, we fuse these graphs in the viewpoint of multi-view through an alternating optimization method and obtain the optimal coefficients of all random subspaces. Finally, we train a linear classifier under the framework of manifold regularization (MR) to obtain the final classified results. Through fusing the random subspaces, the proposed SSM-RSSR can obtain better and more stable results in a wider range of the dimension of random subspace and the number of random subspaces. Extensive experimental results on the several UCI datasets and face image datasets have demonstrated the effectiveness of the proposed SSM-RSSR.

Keywords

Semi-supervised classification Sparse representation Random subspace Multi-view 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61727802, 61601225).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Spectral Imaging and Intelligent SenseNanjing University of Science and TechnologyNanjingChina

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