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The self-organizing map, the Geo-SOM, and relevant variants for geosciences

The self-organizing map, the Geo-SOM, and relevant variants for geosciences. Advisor : Dr. Hsu Presenter : Wun-Cheng Cai Author : Fernando Bacao, Victor Lobo Marco Painho. 2005,CG. Outline. Motivation Objective Method Experience Practical application

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The self-organizing map, the Geo-SOM, and relevant variants for geosciences

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  1. The self-organizing map, the Geo-SOM, and relevant variants for geosciences Advisor : Dr. Hsu Presenter : Wun-Cheng Cai Author : Fernando Bacao, Victor Lobo Marco Painho 2005,CG

  2. Outline • Motivation • Objective • Method • Experience • Practical application • Conclusion • Personal Comments

  3. Motivation • We used SOM dealing with geo-referenced data, but do not consider geographical coordinate.

  4. Objective • We present a new SOM architecture, the Geo-SOM, which was especially designed to take into account spatial dependency.

  5. X be the set of n training patterns x1,x2,..xn, each of these having a set of components geoiand another set ngfi. • W be a p x q grid of units wijwhere i and j are their coordinates on that grid, and each of these units having a set of components wgeoijand another set wngfij. • αa be the learning rate, assuming values in [0,1], initialized to a given initial learning rate • r be the radius of the neighborhood function h(wij,wmn,r), initialized to a given initial radius • k be the radius of the geographical BMU that is to be searched • f be a logical variable that is true if the units are at fixed geographical locations. Method

  6. Experience Z=10 Z=0

  7. Experience Geo-SOM SOM

  8. Practical application Geo-SOM SOM

  9. Conclusion • Geo-SOM suited to deal with geo-referenced data. • We present the Geo-SOM, which enable the treatment of the spatial dimension of the data separately from other attribute data, when performing data reduction tasks.

  10. Personal Comments • Applications • understand geographical state of developing • Advantages • deal with geo-referenced data • Disadvantage • high quantization error

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