Related Papers and Links
*How are these algorihms organized? It is shown in this MindMap

Feature Selection

S.B. Serpico and L. Bruzzone, “A new search algorithm for feature selection in hyperspectral remote sensing images,” IEEE Trans. Geosci. Rem. Sens, 39(7): 1360-1367, 2001.


Feature Extraction

C. Lee and D. Landgrebe, “Decision boundary feature extraction for neural networks,” IEEE Trans. Neural Networks, 8(1): 75-83, 1997.

X. Jia and J.A. Richards, “Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification,” IEEE Trans. Geosci.Rem. Sens., 37(1): 538-42, 1999.


Decision Trees

C4.5: R. Quinlan. C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.

CART: L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth, Belmont, 1984.


Neural Networks

J.A. Benediktsson, P.H. Swain, and O.K. Ersoy, "Neural Network Approaches versus Statistical Methods in Classification of Multisource Remote Sensing Data," IEEE Trans. on Geosci. and Rem.Sens., 28(4), July 1990.

An Introduction to Neural Networks: Some Q&A


Fisher Linear Discriminant

R.A. Fisher, "The Statistical Utilization of Multiple Measurements," Annals of Eugenics, Vol 8, paes 378-386, 1938

Also see BPC and BHC of S. Kumar of our recent published paper


Support Vector Machines

Vapnik, V. The Nature of Statistical Learning Theory, New York:Springer-Verlag, 1995.

C. Cortes and V. Vapnik, "Support-Vector networks," Machine Learning, 20(3): 273-297, 1995.

C.-W. Hsu and C.-J. Lin. "A comparison of methods for multi-class support vector machines," IEEE Transactions on Neural Networks, 13(2002), 415-425

SVM Light: The most popular SVM website that has a collection of SVM codes

LibSVM: An efficient SVM code with Java and C source code.

OSU SVM toolbox: The most popular Matlab SVM code that is based on LibSVM.


Class Decomposition Methods

Pairwise comparision: S. Knerr, L. Personnaz, and G. Dreyfus. "Handwritten digit recognition by neural networks with single-layer training," IEEE Trans. on Neural Networks, 3(1992) 962-968. : The paper that proposed .

A paper that compares different decomposition methods for SVM: C.-W. Hsu and C.-J. Lin. "A comparison of methods for multi-class support vector machines," IEEE Trans. on Neural Networks, 13(2002), 415-425.

Error Correcting Output Codes: T. G. Dietterich and R. Bakiri, "Solving multiclass learning problems using error correcting output codes," J. Artificial Intelligence Research, 2(1): 263-286, 1995.

Kumar, S., and Ghosh, J. 1999. "GAMLS: A generalized framework for associative modular learning systems."

Binary hierarchical classifier (BHC) and GAMLS: S. Kumar, J. Ghosh, and M.M. Crawford, "Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis," International J. Pattern Analysis and Applications, vol. 5, no.2, 210-220, 2002.

Hierarchical support vector machines and max-cut class decomposition: Y. Chen, M.M. Crawford, and J. Ghosh, "Integrating Support Vector Machines in a Hierarchical Output Decomposition Framework," Proc. 2004 International Geoscience and Remote Sensing Symposium, Anchorage, Alaska, Sept 20-24, 949-953, 2004.


Ensemble Methods

Bagging: L. Breiman, "Bagging Predictors," Machine Learning, 26, No. 2, 123-140, 1996

Boosting: R. Schapire, Y. Freund, P. Bartlett and W. Lee, "Boosting the margin: A new explanation for the effectiveness of voting methods," Annals of Statistics, 26(5):1651-1686, 1998.

Random Subspace Method: T. K. Ho, "The random subspace method for constructing decision forests," IEEE Trans. on Pattern Analysis and Machine Intelligence, 20,8, 832-844, 1998

Random Forests: L. Breiman, "Random forests," Machine Learning, 45: 5-32, 2001.

J. Ham, Y. Chen, M. Crawford, and J. Ghosh, "Investigation of the Random Forest Framework for Classification of Hyperspectral Data," IEEE Trans. on Geoscience and Remote Sensing, accepted for publication.


Manifold Learning

Isomap:J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric frame- work for nonlinear dimensionality reduction. Science, 290(5500):2319–2323, 2000.

LLE: S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by local linear embedding. Science, 290(5500):2323–2326, 2000

Y. Chen, M. M. Crawford, and J. Ghosh. Applying nonlinear manifold learning to hyperspectral data for land cover classification. In 2005 International Geosci. and Remote Sens. Symposium, Seoul, South Korea, Jul. 24-29 2005.

C. M. Bachmann, T. L. Ainsworth, and R. A. Fusina. Exploiting manifold geometry in hyperspectral imagery. IEEE Trans. Geosci. and Remote Sens., 43(3):441–454, Mar 2005.


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