___

• A. Mohan, G. Sapiro and E. Bosch, “Spatially coherent Nonlinear Dimensionality Reduction and. Segmentation of Hyperspectral Images,” IEEE Geosci.and Remote Sens., vol. 4, no.2, pp.206-210, April 2007.
• L. Macter, E. Postma, and J. Heik, "Dimensionality reduction:A comparative review", Ticc Tilburg University, October 26, 2009.
• E. Namey, G. Guest, L. Thairy and L. Johnson,“Data reduction techniques for large qualitative data sets”, Handbook for team-based qualitative research, pp.137-162, March 29, 2007.
• Y. Hou, P. Zhang, X. Xu, X. Zhang and W. Li,“Nonlinear dimensionality reduction by Locally Linear Inlaying,” IEEE Transactions of Neural Networks, vol.20, no.2, pp.300-315, February 2009.
• W. Kim, M. Crawford and S. Lee, “Integrating spatial proximity with manifold learning for hyperspectral data," Korean Journal of Remoting Sensing, vol.26, no.6, pp.693-703, 2010.
• Melgani F, M. Bruzone L , “Classification of hyperspectral remote sensing images with support vector machines," IEEE Transactions Geoscieence and Remote Sensing on volume 42, Issue 8, pp 1778-1790, Agust 2004.
• X. Huang, L. Zhang, “A comparative study of spatial approaches for urban mapping using hyperspectral Rosis images over Pavia City,"volume 30, Issue 12, 2009.
• M. Fong, “ Dimension Reduction on Hyperspectral Images”, Project Report, REU projects, UCLA Dept of ath. Agu 2007.
• Melba M. Crawford, Li Ma and W. Kim, “Exploring nonlinear manifold learning for classification of hyperspectral data,” Augmented vision and reality volum 3, pp 207-234, 2011.
• Kim W, “Multiresolution manifold learning for classification of hyperspectral data,” IEEE International Geoscience and Remote Sensing Symposium, pp.3785-3788, July 2007.
• M. Fauvel, J. Benediksson, J. Chanussot and Johannes R.Sveinsson, “Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles,” IEEE Transactions on Geoscience and Remote Sensing Society,vol.46, no.11, pp.30804-3814, Nov 2008.
• Y Chen, M.Crawford and J. Ghost, “Applying nonlinear manifold learning to hyperspectral data for land cover classification,” in Processing of the IEEE International Geoscience and Remote Sensing Symposium, vol.6, pp.4311-4314, 2005.
• S.T.Roweis and L.K.Saul, “Nonlinear dimensionality reduction by Local Linear Embedding,” Science, vol.290, no.5500, pp.2323- 2326, 2000.
• Shen-En Qian, Chen G, “A new nonlinear dimensionality reduction method with application to hyperspectral image analysis,” IEEE International Geoscience and Remote Sensing Symposium, pp.270-273, July 2007.
• Charles M.Banchmann, Thomas L.Ainsworth, Robert A.Fusina, “Exploiting manifold geometry in hyperspectral images,” IEEE Transactions on volume 43,no 3, pp.441-454, March 2005.
• Bo Du, L. Zhang, L. Zhang, T. Chen and Ke Wu, “A discriminative manifold learning based dimension reduction method for hyperspectral classification,” International Journal of Fuzzy Systems, vol.14, no.2, June 2012.
• Costa J.A, Hero A.O,“Classification constrained dimensionality reduction,” IEEE International Conference on Speech and Signal Processing, vol 5, pp. 1077-1080, March 2005.
• Wang X.R, Ramos F, Kaupp T,“Probabilistic classification of hyperspectral images by learning nonlinear dimensionality reduction mapping,” International Conference on Information Fusion, July 2006.
• L. O. Jimenez, and D. A. Landgrebe, "Supervised Classification in High Dimensional Space: Geometrical, Statistical, and Asymptotical Properties of Multivariable Data," IEEE Transactions on Systems, Man and Cybernetics, vol 28, no. 1, pp. 39-54, Feb. 1998.
• J.B. Tenenbaum, V. de Silva, and J.C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, pp. 2319-2323, Dec 2000.