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Related Experiment Video

Updated: Jul 7, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Growing a hypercubical output space in a self-organizing feature map.

H U Bauer¹, T Villmann

¹Int. Comput. Sci. Inst., Berkeley, CA.

IEEE Transactions on Neural Networks

|January 1, 1997

Summary

The growing self-organizing map (GSOM) algorithm adapts output space topology during learning to improve neighborhood preservation. This novel approach enhances data projection for better representation in neural maps.

Related Experiment Videos

Last Updated: Jul 7, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Area of Science:

Computational neuroscience
Machine learning
Data visualization

Background:

Neural maps preserve data topology by mapping input space to output space neurons.
Optimal neighborhood preservation requires output space topology to match input data structure.
Kohonen's self-organizing feature map (SOFM) is a widely used map-learning algorithm.

Purpose of the Study:

To introduce a novel growth algorithm, the growing self-organizing map (GSOM).
To enhance the SOFM by adapting the output space grid during the learning process.
To improve neighborhood preservation in neural maps, especially for data with complex structures.

Main Methods:

Developed the GSOM algorithm, a variant of SOFM that adapts output space dimensionality and shape.
Constrained the GSOM output space to a hypercubical shape, allowing adaptation of grid dimensions.
Applied the GSOM algorithm to three datasets, including two real-world examples.

Main Results:

The GSOM algorithm successfully adapted the output space grid during learning.
GSOM produced neural maps with significantly improved neighborhood preservation compared to standard methods.
Neighborhood preservation achieved by GSOM was found to be nearly optimal.

Conclusions:

The GSOM algorithm offers an effective method for enhancing neural map topology adaptation.
GSOM's flexible output space structure is suitable for various information processing systems.
The developed GSOM approach provides superior neighborhood preservation for complex data structures.