Night-Vision Data Classification Based on Sparse Representation and Random Subspace

  • Lianfa BaiEmail author
  • Jing Han
  • Jiang Yue


The traditional classification method is difficult to achieve good classification results when the training samples are few, and the unsupervised classification algorithms cannot use class information to improve their performance. Thus, it is necessary to apply semi-supervised classification methods. This chapter introduces semi-supervised data classification based on sparse representation and stochastic subspace. First, the dictionary in sparse representation is simplified to improve the speed and accuracy of sparse representation. To meet the needs of a complete dictionary, we combine each random subspace of sparse representation when the dimensions of the original sample are far lower than the random subspace to enhance the ability of the original data. Second, with traditional random subspace methods, all features have the same probability for selection, and a method based on attribute features is introduced, promoting the accuracy of selected key feature probabilities to enhance the final accuracy of data classification.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina
  3. 3.National Key Laboratory of Transient PhysicsNanjing University of Science and TechnologyNanjingChina

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