[1] Chang C I. Hyperspectral imaging: techniques for spectral detection and classification[M]. New York: Plenum, 2003.
[2] Jolliffe L T. Principal Component Analysis[M]. Springer, 2nd ed. 2002.
[3] Hyvarinen, Karhunen J, Oja E. Independent Component Analysis [M]. New York: Wiley, 2001.
[4] Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, 19(7):711-720.
[5] Müller K R, Mika S, R tsch G, et.al. An introduction to kernel-based learning algorithms [J]. IEEE Trans. on Neural Networks, 2001, 12(2):181-201.
[6] Sam T R , Lawrence K S. Nonlinear Dimensionality Reduction by Locally Linear Embedding [J]. Science, 2000, 290(5500): 2323-2326.
[7] Tenenbaum J. B, de Silva.V, Langford J C. A global geometric framework for nonlinear dimensionality reduction [J]. Science, 2000, 290(5500):2319-2323.
[8] Bachmann C M, Ainsworth T L, Fusina R A. Improved Manifold Coordinate Representations of Large-Scale Hyperspectral Scenes[J]. IEEE Trans. on Geosci. Remote Sens., 2006, 44(10):2786-2802.
[9] Wang L, Zhang Y, Feng J. On the Euclidean distance of images [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2005, 27(8):1334-1339.
[10] Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines [J]. IEEE Trans. on Geosci. Remote Sens., 2004, 42(8):1778-1790.
[11] Cover T, Hart P. Nearest neighbor pattern classification [J]. IEEE Trans. on Inf. Theory, 1967, 13(1): 21-27.
[12] Landgrebe D. Multispectral data analysis: A signal theory perspective[M]. West Lafayette: School of Electrical & Computer Engineering, Purdue University, 56-89, 1998.
