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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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.
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.
Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neurocomputing, 15(6), 1373–1396.
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.
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.
Cai, D. He, X., Han, J. (2007). Semi-supervised discriminant analysis. ICCV, 1–7.
Cevikalp, H., Verbeek, J., Jurie, F., & Klaser, A. (2008). Semi-supervised dimensionality reduction using pairwise equivalence constraints. VISAPP, 1, 489–496.
Chen, Z., & Haykin, S. (2002). On different facets of regularization theory. Neurocomputing, 14(12), 2791–2846.
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.
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.
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.
Fan, M., Zhang, X., Lin, Z., Zhang, Z., Bao, H. (2012). Geodesic based semi-supervised multi-manifold feature extraction. ICDM, 852–857.
Fan, M. Y., Gu, N. N., Qiao, H., Zhang, B. (2011). Sparse regularization for semi-supervised classification. Pattern Recognition, 44(8), 1777–1784.
Girosi, F. (1998). An equivalence between sparse approximation and support vector machines. Neurocomputing, 10(6), 1455–1480.
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.
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.
Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Aanalysis and Machine Intelligence, 20(8), 832–844.
Jenatton, R., Mairal, J., Obozinski, G., Bach, F. (2010). Proximal methods for sparse hierarchical dictionary learning. International Conference on Machine Learning, 487–494.
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.
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.
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.
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.
Li, B., Huang, D. S., Wang, C., & Liu, K. H. (2008). Feature extraction using constrained maximum variance mapping. Pattern Recognition, 41(11), 3287–3294.
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.
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A. (2008). Supervised dictionary learning. Conference on Neural Information Processing Systems, 1–8.
Mairal, J., Jenatton, R., Obozinski, G., Bach, F. (2010). Network flow algorithms for structured sparsity. Conference on Neural Information Processing Systems, 1558–1566.
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.
Martinez, A. M., Kak, A. C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine intelligence, 23(2), 228–233.
Martinez, A. M., Benavente R. (1998). The AR face database. CVC Technical Report (p. 24).
Poggio, T., & Girosi, F. (1990b). Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247(4945), 978–982.
Poggio, T., Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE. 78(9), 1481–1497.
Protter, M., & Elad, M. (2009). Image sequence denoising via sparse and redundant representations. IEEE Transactions on Image Processing, 18(18), 27–35.
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.
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.
Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimension reduction by locally linear embedding. Science, 290(5), 2323–2326.
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).
Scholkopf, B., Herbrich, R., & Smola, A. J. (2000). A generalised representer theorem. Conference on Computational Learning Theory, 42(3), 416–426.
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.
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.
Sim, T., Baker, S., & Bsat, M. (2003b). The CMU pose, illumination, and expression database. TPAMI., 25(12), 1615–1618.
Tenenbaum, J. B., Silva, V. D., & Langform, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5000), 2319–2323.
Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society, 58(1), 267–288.
Tikhonov, A. N., Arsenin, V. Y. (1977). Solutions of ill-posed problems. Vh Winston.
Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley.
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.
Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1), 37–52.
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.
Wu, F., Wang, W., Yang, Y., Zhuang, Y., & Nie, F. (2010). Classification by semi-supervised discriminative regularization. Neurocomputing, 73(10), 1641–1651.
Xue, H., Chen, S., & Yang, Q. (2009). Discriminatively regularised least-squares classification. Pattern Recognition, 42(1), 93–104.
Yan, S., Wang, H. (2009). Semi-supervised learning by sparse representation. SIAM International Conference on Data Mining, 792–801.
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.
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.
Yin, Q. Y., Wu, S., He, R., & Wang, L. (2015). Multi-view clustering via pairwise sparse subspace representation. Neurocomputing, 156, 12–21.
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.
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.
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.
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.
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.
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.
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.
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.
Zhu, X. (2005). Semi-supervised learning literature survey.
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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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