Weather data mining using independent component analysis

Journal of Machine Learning Research

Volumn 5, Issue , 2004, Pages 239-253

  Basak, Jayanta ^a   Sudarshan, Anant ^a   Trivedi, Deepak ^a   Santhanam, M S ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^b MAX PLANCK INSTITUT FÜR PHYSIK KOMPLEXER SYSTEME   (Germany)

Author keywords

ICA; North Atlantic Oscillation; Spatio temporal pattern mining

Indexed keywords

ATMOSPHERIC PRESSURE; INDEPENDENT COMPONENT ANALYSIS; METEOROLOGY;

INDEPENDENT COMPONENT ANALYSIS TECHNIQUE; INDEPENDENT COMPONENTS; NAO INDEX; NORTH ATLANTIC OSCILLATIONS; SPATIO-TEMPORAL DATA; SPATIOTEMPORAL PATTERNS; SYNOPTIC WEATHER PATTERNS; WEATHER DATA;

DATA MINING;

EID: 33645977843     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. S.-I. Amari. Natural gradient works efficiently in learning. Neural computation, 10:251-276, 1998.
2. S.-I. Amari, A. Cichocki, and H. H. Yang. A new learning algorithm for blind signal separation. In D. S. Touretzky, M. C. Mozer, and E. Hasselmo, editors, Neural Information Processing Systems: Natural and Synthetic, NIPS'96, pages 757-763, MIT Press, 1996.
3. J. Basak and S.-I. Amari. Blind separation of a mixture of uniformly distributed signals. Neural Computation, 11:1011-1034, 1999.
4. A. J. Bell and T. J. Sejnowski. An information maximization approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995.
5. A. J. Bell and T. J. Sejnowski. The 'independent components' of natural scenes are edge filters. Vision Research, 37(23):3327-3338, 1997.
6. I. Bell and M. Visbeck. North Atlantic Oscillation. URL http://www.ldeo.columbia.edu/NAO.
7. J. F. Cardoso and B. Laheld. Equivariant adaptive source separation. IEEE Transactions on Signal Processing, 44:3017-3030, 1996.
8. P. A. Dejviver and J. Kittler. Pattern Recognition: A Statistical Approach. Prentice Hall International, 1982.
9. W. W. Hsieh and B. Tang. Applying neural network models to prediction and data analyis in meteorology and oceanography. Bulletin of America Meteorological Society, 79:1855-1870, 1998.
10. J. W. Hurrell. Decadal trends in the North Atlantic Oscillation region temperatures and precipitation. Science, 269:676-679, 1995.
11. A. Hyvarinen. Complexity pursuit: Separating interesting components from time-series. Neural Computation, 13:883-898, 2001.
12. A. Hyvarinen and E. Oja. A fast fixed point algorithm for ICA. Technical Report A-35, Faculty of Information Technology, Helsinki University of Technology, Finland, 1996.
13. A. Hyvarinen and E. Oja. Independent component analysis by general nonlinear Hebbian-like learning rules. Signal Processing, 64:301-313, 1998.
14. C. Jutten and J. Herault. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24:1-20, 1991.
15. J. Karhunen and J. Joutsensalo. Representation and separation of signals using nonlinear pca type learning. Neural Networks, 7:113-127, 1994.
16. P. J. Lamb and R. A. Peppler. North Atlantic Oscillation - concept and an application. Bulletin of American Meteorological Society, 68:1218-1225, 1987.
17. E. N. Lorenz. Deterministic non-periodic flow. Journal of Atmospheric Sciences, 20:130-141, 1963.
18. E. N. Lorenz. A study of the predictability of a 28-variable atmospheric model. Tellus, 17:321-329, 1965.
19. A. H. Monahan. Nonlinear principal component analysis by neural networks: Theory and applications to the Lorentz system. Journal of Climate, 13:821-835, 2000.
20. Climate Diagnostics Center: NOAA-CIRES. URL http://www.cdc.noaa.gov.
21. E. Oja. A simplified neuron model as a principal component analyzer. Journal of Mathematical Biology, 15:267-273, 1982.
22. E. Oja. The nonlinear PCA learning rule and signal separation - mathematical analysis. Technical Report A26, Helsinki University of Technology, Lab. of Computer and Information Science, 1995.
23. E. Oja, J. Karhunen, L. Wang, and R. Vigario. Principal and independent components in neural networks - recent developments. In Proc. Italian Workshop on Neural Networks, WIRN'95, Vietri, Italy, 1995.
24. M. S. Santhanam and P. K. Patra. Statistics of atmospheric correlations. Physical Review E, 64:016102-1-7, 2001.
25. J. V. Stone, J. Porrill, C. Buchel, and K. Friston. Spatial, temporal, and spatiotemporal independent component analysis of fMRI data. In 18th Leeds Statistical Research Workshop on Spatio-Temporal Modeling and its Applications, University of Leeds, 1999.
26. M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3:71-86, 1991.
27. J. M. Wallace and D. S. Gutzler. Teleconnections in the geopotential height field during the northern hemisphere winter. Monthly Weather Review, 109:784-812, 1981.
28. D. S. Wilks. Statistical Methods in Atmospheric Sciences. Academic Press, London, 1995.
29. H. H. Yang and S.-I. Amari. Adaptive on-line learning algorithms for blind separation - maximum entropy and minimum mutual information. Neural Computation, 9:1457-1482, 1997.