Reducing dimensionality of hyperspectral data with diffusion maps and clustering with k-means and Fuzzy ART

International Journal of Systems, Control and Communications

Volumn 3, Issue 3, 2011, Pages 232-251

  Du Plessis, Louis ^a   Xu, Rui ^b   Damelin, Steven ^a,c   Sears, Michael ^a   Wunsch II, Donald C ^d  

^a UNIVERSITY OF THE WITWATERSRAND   (South Africa)

^b GE GLOBAL RESEARCH   (United States)

^c GEORGIA SOUTHERN UNIVERSITY   (United States)

^d Architectural and Environmental Engineering   (United States)

Author keywords

Clustering; Diffusion maps; Dimensionality reduction; Fuzzy ART; Hyperspectral data; K means; Markov systems; Mineral classification; Neural networks

Indexed keywords

EID: 84861383934     PISSN: 17559340     EISSN: 17559359     Source Type: Journal    
DOI: 10.1504/IJSCC.2011.042430     Document Type: Article

References (23)

1. Bell, G. (2008) 'Bell's law for the birth and death of computer classes: A theory of the computer's evolution', Communications of the ACM, Vol. 51, No. 1, pp.86-94.
2. Bengio, Y., Paiement, J-F., Vincent, P., Delalleau, O., Roux, N.L. and Ouimet, M. (2004) 'Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering', Advances in Neural Information Processing Systems, Vol.16, pp.177-184.
3. Bowles, J.H. and Gillis, D.B. (2007) 'An optical real-time adaptive spectral identification systems (ORASIS)', in Chang, C-I. (Ed.): Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons, Hoboken, NJ, pp.77-106.
4. Carpenter, G. and Grossberg, S. (1987) 'A massively parallel architecture for a self-organizing neural pattern recognition machine', Computer Vision, Graphics, and Image Processing, Vol. 37, pp.54-115.
5. Carpenter, G., Grossberg, S. and Rosen, D. (1991) 'Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance', Neural Networks, Vol. 4, pp.759-771.
6. Cawse, K., Damelin, S., du Plessis, L., McIntyre, R., Mitchley, M. and Sears, M. (2008) 'An investigation of data compression techniques for hyperspectral core imager data', Proceedings of the Mathematics in Industry Study Group, South Africa - MISG2008, Wits, Johannesburg.
7. Coifman, R.R. and Lafon, S. (2006) 'Diffusion maps', Journal of Applied and Computational Harmonic Analysis, April, pp.5-30. (Pubitemid 43947619)
8. Dean, J. and Ghemawat, S. (2008) 'MapReduce: Simplified data processing on large clusters', Communications of the ACM, Vol. 51, No. 1, January, pp.107-113.
9. Grossberg, S. (1976) 'Adaptive pattern recognition and universal encoding II: Feedback, expectation, olfaction, and illusions', Biological Cybernetics, Vol. 23, pp.187-202.
10. Huang, J., Georgiopoulos, M. and Heileman, G. (1995) 'Fuzzy ART properties', Neural Networks, Vol. 2, pp.203-213.
11. Keller, Y., Lafon, S. and Krauthammer, M. (2006) 'Protein cluster analysis via directed diffusion', Classification Society of North America 2006 Meeting on Network Data Analysis and Data Mining: Applications in Biology, Computer Science, Intrusion Detection, and Other Areas, Rutgers University, Piscataway, NJ.
12. Kerekes, J.P. and Schott, J.R. (2007) 'Hyperspectral imaging systems', in Chang, C-I. (Ed.): Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons, Hoboken, NJ, pp.19-45.
13. Lafon, S., Keller, Y. and Coifman, R. (2006) 'Data fusion and multicue data matching by diffusion maps', IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, pp.1784-1797. (Pubitemid 46405426)
14. Lafon, S. and Lee, A.B. (2006) 'Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning and data set parameterization', IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 9, September, pp.1393-1403. (Pubitemid 44132647)
15. Ma, Y., Damelin, S., Masoud, O. and Papanikolopoulos, N. (2006) 'Activity recognition via classification constrained diffusion maps', in Bebis, G. et al. (Ed.): Advances in Visual Computing, LNCS 4291, Springer-Verlag, Berlin/Heidelberg, pp.1-8. (Pubitemid 44891532)
16. Meuth, R. (2007) 'GPUs surpass computers at repetitive calculations', IEEE Potentials, Vol. 26, No. 6, pp.12-15.
17. Mulder, S. and Wunsch II, D. W. (2003) 'Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks', Neural Networks, Vol. 16, pp.827-832. (Pubitemid 36794358)
18. Ripley, B. (1996) Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, UK.
19. Roweis, S. and Saul, L. (2000) 'Nonlinear dimension reduction by locally linear embedding', Science, Vol. 290, No. 5500, pp.2323-2326. (Pubitemid 32041578)
20. Tenenbaum, J., de Silva, V. and Langford, J. (2000) 'A global geometric framework for nonlinear dimensionality reduction', Science, Vol. 290, No. 5500, pp.2319-2323. (Pubitemid 32041577)
21. Xu, R. and Wunsch II, D.W. (2009) Clustering, IEEE/Wiley, Hoboken, NJ.
22. Xu, R., Damelin, S. and Wunsch II, D.C. (2007) 'Applications of diffusion maps in gene expression data-based cancer diagnosis analysis', Engineering in Medicine and Biology Society, 2007. EMBS 2007, 29th Annual International Conference of the IEEE, August, pp.4613-4616.
23. Xu, R., du Plessis, L., Damelin, S., Sears, M. and Wunsch II, D.C. (2009) 'Analysis of hyperspectral data with diffusion maps and fuzzy ART', Proceedings of the IJCNN 2009, June, pp.3390-3397.