presenter : yu-ting lu authors : ezequiel lópez -rubio 2013. tnnls

Intelligent Database Systems Lab

Presenter : YU-TING LU

Authors : Ezequiel López-Rubio

2013. TNNLS

Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


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.


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.


Methodology-basic concepts• Review of Two Self-Organizing Map Models


Methodology-basic concepts• Types of Topology Errors


Methodology-basic concepts• Self-Intersections

i

j k

r t

s


Methodology – self-intersection avoidance


Experiments


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.


Comments• Advantages

-Improving the Quality of Self-Organizing Maps

• Applications- SOM

presenter : yu-ting lu authors : ezequiel lópez -rubio 2013. tnnls

Documents

generated maps

number of topology errors

good quality map

yuting luauthors

better insight

key issue

ezequiel lpezrubio2013

larger quantization