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Auto-SOM: recursive parameter estimation for guidance of self-organizing feature maps.

K Haese¹, G J Goodhill

¹Data Warehouse/Data Mining, Mummert & Partners Management Consulting, Braunschweig, D-38104, Germany.

Neural Computation

|March 13, 2001

Summary

Auto-SOM automatically tunes parameters for Kohonen

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Area of Science:

Computational intelligence
Machine learning
Data visualization

Background:

Kohonen's self-organizing map (SOM) is vital for dimensionality reduction and exploratory data analysis.
Traditional SOMs require manual, heuristic parameter tuning for optimal performance.
Neighborhood preservation is crucial for accurate low-dimensional representations.

Purpose of the Study:

To introduce Auto-SOM, an algorithm for automatic learning parameter estimation in SOMs.
To enable user-defined control over neighborhood violation degrees in mappings.
To enhance the neighborhood preservation capabilities of self-organizing maps.

Main Methods:

Auto-SOM integrates a Kalman filter-based SOM with recursive parameter estimation.
Kalman filter optimizes neuron weights using estimated learning coefficients to minimize estimation error variance.
Neighborhood function width is estimated by minimizing Kalman filter prediction error variance, incorporating a topographic function to prevent violations.

Main Results:

Auto-SOM successfully automates the estimation of critical learning parameters for SOMs.
The algorithm effectively prevents neighborhood violations to a user-specified degree in both mapping directions.
Demonstrated preservation of neighborhood structures in low-dimensional mappings through three application examples.

Conclusions:

Auto-SOM significantly improves the usability and performance of self-organizing maps.
This automated approach facilitates the creation of reliable neighborhood-preserving maps for dimension reduction.
The method offers a robust solution for exploratory data analysis requiring accurate neighborhood preservation.

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