Abstract
LLE is a nonlinear dimensionality reduction method, which has been successfully applied to data visualization. Based on the assumption of local linearity, LLE can compute the weights between the KNN nodes using the local least reconstruction errors, which increase the computational cost. In this paper, a method titled Discriminant Graph Based Linear Embedding (DGBLE) is proposed to set the weights between the nodes in the KNN graph directly to reduce the computational expense. Moreover, label information can also be taken into account to improve the discriminant power of the original LLE. Experiments on some benchmark data show that the proposed method is feasible and effective.
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References
Jain, A.K., Duin, R.P.W., Mao, J.C.: Statistical Pattern Recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 585–591. MIT Press, Cambridge (2002)
Biometrics Research Centre, http://www4.comp.polyu.edu.hk/~biometrics
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)
He, X., Yang, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face Recognition Using Laplacianfaces. IEEE Trans. Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)
Kokiopoulou, E., Saad, Y.: Orthogonal Neighborhood Preserving Projections. In: The Fifth IEEE International Conference on Data Mining, pp. 1–7. IEEE Press, New York (2005)
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Li, B., Liu, J., Dong, WY., Zhang, WS. (2012). Discriminant Graph Based Linear Embedding. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_52
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DOI: https://doi.org/10.1007/978-3-642-31588-6_52
Publisher Name: Springer, Berlin, Heidelberg
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