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

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Night Vision Processing and Understanding

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Abstract

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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References

  • Aleksandar, D., & Qiu, K. (2010). Automatic hard thresholding for sparse signal reconstruction from NDE measurements. Review of Progress in Quantitative Nondestructive Evaluation, 29(1211), 806–813.

    Google Scholar 

  • Belhumeur, P. N., Hespanha, J. P., & Kriegman D. J. (1997). Eigenfaces versus fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine intelligence, 19(7), 711–720.

    Article  Google Scholar 

  • Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neurocomputing, 15(6), 1373–1396.

    MATH  Google Scholar 

  • Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization, a geometric framework for learning from labelled and unlabelled examples. Journal on Machine Learning Research, 7, 2399–2434.

    MATH  Google Scholar 

  • Bezdek, J. C., Hathaway, R. J. (2002). Some notes on alternating optimisation. In AFSS International Conference on Fuzzy Systems (pp. 288–300). Springer Berlin Heidelberg. http://archive.ics.uci.edu/ml/datasets.html.

    Chapter  Google Scholar 

  • Cai, D. He, X., Han, J. (2007). Semi-supervised discriminant analysis. ICCV, 1–7.

    Google Scholar 

  • Cevikalp, H., Verbeek, J., Jurie, F., & Klaser, A. (2008). Semi-supervised dimensionality reduction using pairwise equivalence constraints. VISAPP, 1, 489–496.

    Google Scholar 

  • Chen, Z., & Haykin, S. (2002). On different facets of regularization theory. Neurocomputing, 14(12), 2791–2846.

    MATH  Google Scholar 

  • Chen, K., & Wang, S. H. (2011). Semi-supervised learning via regularised boosting working on multiple semi-supervised assumptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1), 129–143.

    Article  Google Scholar 

  • Drori, I., Donoho, D. L. (2006). Solution of L1 minimisation problems by LARS homotopy methods. IEEE International Conference on Acoustics, Speech and Signal Processing, 636–639.

    Google Scholar 

  • Fan, M. Y., Zhang, X. Q., Lin, Z. C., Zhang, Z. F., & Bao, H. J. (2014). A regularised approach for geodesic-based semisupervised multimanifold learning. IEEE Transactions on Image Processing, 23(5), 2133–2147.

    Article  MathSciNet  MATH  Google Scholar 

  • Fan, M., Zhang, X., Lin, Z., Zhang, Z., Bao, H. (2012). Geodesic based semi-supervised multi-manifold feature extraction. ICDM, 852–857.

    Google Scholar 

  • Fan, M. Y., Gu, N. N., Qiao, H., Zhang, B. (2011). Sparse regularization for semi-supervised classification. Pattern Recognition, 44(8), 1777–1784.

    Article  MATH  Google Scholar 

  • Girosi, F. (1998). An equivalence between sparse approximation and support vector machines. Neurocomputing, 10(6), 1455–1480.

    Google Scholar 

  • Han, J., Yue, J., Zhang, Y., & Bai, L. F. (2014). Kernel maximum likelihood-scaled locally linear embedding for night vision images. Optics & Laser Technology, 56(1), 290–298.

    Article  Google Scholar 

  • He, X. F., Yan, S. C., Hu, Y. X., Niuogi, P., & Zhang, H. J. (2005). Face recognition using laplacian faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3), 328–340.

    Article  Google Scholar 

  • Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Aanalysis and Machine Intelligence, 20(8), 832–844.

    Article  Google Scholar 

  • Jenatton, R., Mairal, J., Obozinski, G., Bach, F. (2010). Proximal methods for sparse hierarchical dictionary learning. International Conference on Machine Learning, 487–494.

    Google Scholar 

  • Kim, K. I., & Kwon, Y. (2010). Single-image super-resolution using sparse regression and natural image prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1127–1133.

    Article  Google Scholar 

  • Lai, Z. H., Wan, M. H., Jin, Z., & Yang, J. (2011). Sparse two dimensional local discriminant projections for feature extraction. Neurocomputing, 74(4), 629–637.

    Article  Google Scholar 

  • Lai, Z. H., Wong, W. K., Jin, Z., Yang, J., & Xu, Y. (2012). Sparse approximation to the eigensubspace for discrimination. IEEE Transactions on Neural Networks and Learning Systems, 23(12), 1948–1960.

    Article  Google Scholar 

  • Lee, K. C., Ho, J., & Kriegman, D. J. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 684–698.

    Article  Google Scholar 

  • Li, B., Huang, D. S., Wang, C., & Liu, K. H. (2008). Feature extraction using constrained maximum variance mapping. Pattern Recognition, 41(11), 3287–3294.

    Article  MATH  Google Scholar 

  • Liu, W. F., Tao, D. C., Cheng, J., & Tang, Y. Y. (2014). Multiview Hessian discriminative sparse coding for image annotation. Computer Vision and Image Understanding, 118, 50–60.

    Article  Google Scholar 

  • Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A. (2008). Supervised dictionary learning. Conference on Neural Information Processing Systems, 1–8.

    Google Scholar 

  • Mairal, J., Jenatton, R., Obozinski, G., Bach, F. (2010). Network flow algorithms for structured sparsity. Conference on Neural Information Processing Systems, 1558–1566.

    Google Scholar 

  • Mallapragada, P. K., Jin, R., Jain, A. K., & Liu Semiboost, Y. (2009). Boosting for semi-supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.

    Article  Google Scholar 

  • Martinez, A. M., Kak, A. C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine intelligence, 23(2), 228–233.

    Article  Google Scholar 

  • Martinez, A. M., Benavente R. (1998). The AR face database. CVC Technical Report (p. 24).

    Google Scholar 

  • Poggio, T., & Girosi, F. (1990b). Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247(4945), 978–982.

    Article  MathSciNet  MATH  Google Scholar 

  • Poggio, T., Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE. 78(9), 1481–1497.

    Article  MATH  Google Scholar 

  • Protter, M., & Elad, M. (2009). Image sequence denoising via sparse and redundant representations. IEEE Transactions on Image Processing, 18(18), 27–35.

    Article  MathSciNet  MATH  Google Scholar 

  • Qiao, L. S., Chen, S. C., & Tan, X. Y. (2010). Sparsity preserving discriminant analysis for single training image face recognition. Pattern Recognition Letters, 31, 422–429.

    Article  Google Scholar 

  • Ramirez, I., Sprechmann, P., Sapiro, G. (2010). Classification and clustering via dictionary learning with structured incoherence and shared features. IEEE Conference on Computer Vision and Pattern Recognition, 3501–3508.

    Google Scholar 

  • Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimension reduction by locally linear embedding. Science, 290(5), 2323–2326.

    Article  Google Scholar 

  • Samaria, F., Harter, A. (1994). Parametreisation of a stochastic model for human face identification. In Proceedings of the second IEEE workshop on applications of computer vision (pp. 138–142).

    Google Scholar 

  • Scholkopf, B., Herbrich, R., & Smola, A. J. (2000). A generalised representer theorem. Conference on Computational Learning Theory, 42(3), 416–426.

    MATH  Google Scholar 

  • Shi, C. J., Ruan, Q. Q., An, G. Y., Ge, C., & An, G. (2015). Semi-supervised sparse feature selection based on multi-view Laplacian regularization. Image and Vision Computing, 41, 1–10.

    Article  Google Scholar 

  • Sim, T., Baker, S., & Bsat, M. (2003a). The CMU pose illumination and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1615–1618.

    Article  Google Scholar 

  • Sim, T., Baker, S., & Bsat, M. (2003b). The CMU pose, illumination, and expression database. TPAMI., 25(12), 1615–1618.

    Article  Google Scholar 

  • Tenenbaum, J. B., Silva, V. D., & Langform, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5000), 2319–2323.

    Article  Google Scholar 

  • Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society, 58(1), 267–288.

    MathSciNet  MATH  Google Scholar 

  • Tikhonov, A. N., Arsenin, V. Y. (1977). Solutions of ill-posed problems. Vh Winston.

    Google Scholar 

  • Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley.

    MATH  Google Scholar 

  • Wechsler, H., Phillips, P. J., Bruce, V., Fogelman, F., & Huang, T. S. (1998). Face recognition: from theory to applications. NATO ASI Series F. Computer and Systems Sciences, 163, 446–456.

    Google Scholar 

  • Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1), 37–52.

    Article  Google Scholar 

  • Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine intelligence, 31(2), 210–227.

    Article  Google Scholar 

  • Wu, F., Wang, W., Yang, Y., Zhuang, Y., & Nie, F. (2010). Classification by semi-supervised discriminative regularization. Neurocomputing, 73(10), 1641–1651.

    Article  Google Scholar 

  • Xue, H., Chen, S., & Yang, Q. (2009). Discriminatively regularised least-squares classification. Pattern Recognition, 42(1), 93–104.

    Article  MATH  Google Scholar 

  • Yan, S., Wang, H. (2009). Semi-supervised learning by sparse representation. SIAM International Conference on Data Mining, 792–801.

    Google Scholar 

  • Yang, W. K., Sun, C. Y., & Zhang, L. (2011). A multi-manifold discriminant analysis method for image feature extraction. Pattern Recognition, 44(8), 1649–1657.

    Article  MATH  Google Scholar 

  • Yang, J. C., Wright, J., Huang, T., Ma, Y. (2008). Image super-resolution as sparse representation of raw image patches. IEEE Conference on Computer Vision and Pattern Recognition, 2378–2385.

    Google Scholar 

  • Yin, Q. Y., Wu, S., He, R., & Wang, L. (2015). Multi-view clustering via pairwise sparse subspace representation. Neurocomputing, 156, 12–21.

    Article  Google Scholar 

  • Yu, G. X., Zhang, G., Domeniconi, C., Yu, Z. W., & You, J. (2012a). Semi-supervised classification based on random subspace dimensionality reduction. Pattern Recognition, 45(3), 1119–1135.

    Article  MATH  Google Scholar 

  • Yu, G. X., Zhang, G., Yu, Z. W., Domeniconi, C., You, J., & Han, G. Q. (2012b). Semi-supervised ensemble classification in subspaces. Applied Soft Computing, 12(5), 1511–1522.

    Article  Google Scholar 

  • Yu, G. X., Zhang, G. J., Zhang, Z. L., Yu, Z. W., & Lin, D. (2015). Semi-supervised classification based on subspace sparse representation. Knowledge and Information Systems, 43(1), 81–101.

    Article  Google Scholar 

  • Zhao, M. B., Chow, T. W. S., Zhou, W., Zhang, Z., & Li, B. (2014). Automatic image annotation via compact graph based semi-supervised learning. Knowledge-Based Systems, 76, 148–165.

    Article  Google Scholar 

  • Zhao, M. B., Zhan, C., Wu, Z., & Tang, P. (2015). Semi-supervised image classification based on local and global regression. IEEE Signal Processing Letters, 22(10), 1666–1670.

    Article  Google Scholar 

  • Zhao, Z., Bai, L., Zhang, Y. et al. (2018). Probabilistic semi-supervised random subspace sparse representation for classification. Multimedia Tools and Applications. Springer, pp. 1–27.

    Google Scholar 

  • Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in Neural Information Processing Systems, 16(16), 321–328.

    Google Scholar 

  • Zhu, X. J., Ghahramani, Z. B., Lafferty, J. D. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. International Conference on Machine Learning, 912–919.

    Google Scholar 

  • Zhu, X. (2005). Semi-supervised learning literature survey.

    Google Scholar 

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Authors and Affiliations

  1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

    Lianfa Bai

  2. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

    Jing Han

  3. National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

    Jiang Yue

Authors
  1. Lianfa Bai
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  2. Jing Han
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  3. Jiang Yue
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Correspondence to Lianfa Bai .

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Bai, L., Han, J., Yue, J. (2019). Night-Vision Data Classification Based on Sparse Representation and Random Subspace. In: Night Vision Processing and Understanding. Springer, Singapore. https://doi.org/10.1007/978-981-13-1669-2_5

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  • DOI: https://doi.org/10.1007/978-981-13-1669-2_5

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  • Online ISBN: 978-981-13-1669-2

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