Publisher
Springer International Publishing
Reference36 articles.
1. Biao, H., Zhang, X., Ye, Q., & Zheng, Y. (2013). A novel method for hyperspectral image classification based on Laplacian Eigenmap pixels distribution-flow. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1602–1618.
2. Cao, L. J., Chua, K. S., Chong, W. K., Lee, H. P., & Gu, Q. M. (2003). A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing, 55, 321–336.
3. Devijver, P. A., & Kittler, J. (1982). Pattern recognition: A statistical approach (p. 1e). Prentice-Hall International.
4. Ding, L., Tang, P., & Li, H. (2013). Isomap-based subspace analysis for the classification of hyperspectral data. In Proceedings of IEEE IGARSS, pp. 429–432.
5. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd ed.). Wiley Interscience.