Presenter : Yu-Ting LU Authors : Ezequiel López -Rubio 2013. TNNLS

1. Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance Presenter : Yu-Ting LUAuthors : EzequielLópez-Rubio2013. TNNLS

2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

3. Motivation • The quality of self-organizing maps is always a key issue to practitioners. • This is advantageous as a good quality map provides a better insight to the structure of the input data set.

4. Objectives • Improve the already existing self-organizing models by decreasing the topology errors of the generated maps. • Modify the learning algorithm of self-organizing maps to reduce the number of topology errors.

5. Methodology-basic concepts • Review of Two Self-Organizing Map Models BMU x(k)

6. Methodology-basic concepts • Types of Topology Errors

7. Methodology-basic concepts • Self-Intersections i r t j k s

8. Methodology – self-intersection avoidance

9. Experiments

10. Experiments

11. Experiments

12. Experiments

13. Experiments

14. Experiments

15. Experiments

16. Experiments

17. Experiments

18. Experiments

19. Conclusions • The maps trained with this approach exhibited less topology errors at the expense of a larger quantization error. • The procedure can be easily extended to many self-organizing neural networks, and it does not change the structure of the original model.

20. Comments • Advantages • Improving the Quality of Self-Organizing Maps • Applications - SOM