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

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Self-enhancement learning: target-creating learning and its application to self-organizing maps.

Ryotaro Kamimura¹

¹IT Education Center, Tokai University, 1117 Kitakaname, Hiratsuka, Kanagawa 259-1292, Japan. ryo@keyaki.cc.u-tokai.ac.jp

Biological Cybernetics

|May 20, 2011

Summary

This study introduces self-enhancement learning, a novel neural network method where targets are internally generated. Applied to self-organizing maps, it improved data clustering and neighborhood preservation compared to conventional methods.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Traditional neural network learning relies on external targets.
A need exists for self-directed learning mechanisms within neural networks.

Purpose of the Study:

To introduce and evaluate a novel self-enhancement learning method for neural networks.
To explore the application of self-enhancement learning to self-organizing maps (SOMs).

Main Methods:

Developed a neural network with distinct enhanced and relaxed states.
Utilized Kullback-Leibler divergence and free energy to minimize the gap between states.
Integrated self-enhancement learning into self-organizing maps with lateral interactions.

Main Results:

Self-enhancement learning applied to SOMs produced comparable U-matrices to conventional SOMs.
Improved clarity of class boundaries in housing and cancer datasets.
Achieved better performance in quantitative and topological errors for most datasets.
Enhanced trustworthiness and continuity (neighborhood preservation).

Conclusions:

Self-enhancement learning offers a viable alternative for internal target generation in neural networks.
The method demonstrates comparable or superior performance to conventional SOMs and modern dimensionality reduction techniques.
Further research can explore broader applications of this self-directed learning paradigm.