Multivariate Gaussianization for Data Processing
Multivariate Gaussianization for Data Processing
Multivariate Gaussianization for Data Processing
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Intro Iterative <strong>Gaussianization</strong> Experiments Conclusionshttp://isp.uv.es/soft.htmReferencesReferencesV. Laparra, G. Camps-Valls and J. Malo, “Iterative <strong>Gaussianization</strong>: fromICA to Random Rotations”. IEEE Trans. Neural Networks (2011)V. Laparra, G. Camps-Valls and J. Malo, “PCA <strong>Gaussianization</strong> <strong>for</strong> Image<strong>Processing</strong>”. ICIP (2009)V. Laparra, J. Muñoz-Marí, G. Camps-Valls and J. Malo, “PCA<strong>Gaussianization</strong> <strong>for</strong> One-Class Remote Sensing Image Classification”,SPIE: Europe Remote Sensing (2009)D. W. Scott, <strong>Multivariate</strong> Density Estimation: Theory, Practice, andVisualization, Wiley & Sons, 1992.J.H. Friedman and J.W. Tukey, “A projection pursuit algorithm <strong>for</strong>exploratory data analysis,” IEEE Trans. Comp., vol. C-23, no. 9, pp.881–890, 1974.P. J. Huber, “Projection pursuit,” The Annals of Statistics, vol. 13, no. 2,pp. 435–475, 1985.G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality ofdata with neural networks,” Science, 313(5786), 504–507, 2006.
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