Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

Modular network SOM

Presenter : Cheng-Feng Weng

Authors :Kazuhiro Tokunaga, Tetsuo Furukawa

2009/05/21

NN.9 (2009)

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Outline

Motivation Objective Method Experiments Conclusion Comments

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Motivation

The conventional SOM can only deal with vectorized data. If one wishes to deal with a nonvector dataset, then one

needs to make the data vectorized in advance or modify the SOM itself to adapt to the data type.

New York Tokyo

We only can know they are similar. But no more information about that.

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Objective

It develops a generalized framework of an SOM called a modular network SOM (mnSOM). Every vector unit is replaced by a trainable functional

module such as a neural network.

Choose a module what you want

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The mnSOM with MLP modules

The MLP is multi-layer perceptrons.

Determine the BMM(BMU)…(2)

Distance measure…(1)

Learning weight…(3)

Energy function for the SOM…(4)

Adapt the MLP using back-propagtion method…(5)

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The process of mnSOM

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10.5

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(1)(2)(3)(4) batch SOM

(5)Update MLP

Data

Finished map

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Experiment 1

The example if a family of cubic functions y=ax^3+bx^2+cx

I=6, J=200 I=126,J=8

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Experiments 1(cont.)

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Experiment for weather map

A period of 100days of the year2000 at 20 cities.The 10 cities for training and others for testing.

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Experiment for weather map(cont.)

Preserving the topology of geo. map

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Conclusion

It’s also possible to vectorize the function shapes since the experimenter knows those functions in advance. This means that the feature map of the cubic function

family can be generated by the conventional SOM as well.

The advantages of mnSOMs: Every module in an mnSOM has the capability of information

processing. The mnSOM also provides a way of fusing a supervised and an

unsupervised learning algorithm.

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Comments Advantage

Interesting expriments. The concept is simple.

Drawback Applying other modules is inconvenient.

Application Time serial data.