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Probabilistic semi-supervised random subspace sparse representation for classification

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Abstract

In this paper, we present a novel approach for classification named Probabilistic Semi-supervised Random Subspace Sparse Representation (P-RSSR). In many random subspaces based methods, all features have the same probability to be selected to compose the random subspace. However, in the real world, especially in images, some regions or features are important for classification and some are not. In the proposed P-RSSR, firstly, we calculate the distribution probability of the image and determine which feature is selected to compose the random subspace. Then, we use Sparse Representation (SR) to construct graphs to characterize the distribution of samples in random subspaces, and train classifiers under the framework of Manifold Regularization (MR) in these random subspaces. Finally, we fuse the results in all random subspaces and obtain the classified results through majority vote. Experimental results on face image datasets have demonstrated the effectiveness of the proposed P-RSSR.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61727802, 61501235), the National Defense Pre-Research Field Foundation of China (6140450010316BQ02001).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing, 210094, China

    Zhuang Zhao, Lianfa Bai, Yi Zhang & Jing Han

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  1. Zhuang Zhao
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  2. Lianfa Bai
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  3. Yi Zhang
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Correspondence to Lianfa Bai.

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Zhao, Z., Bai, L., Zhang, Y. et al. Probabilistic semi-supervised random subspace sparse representation for classification. Multimed Tools Appl 77, 23245–23271 (2018). https://doi.org/10.1007/s11042-017-5567-z

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