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

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Multivariate student-t self-organizing maps.

Ezequiel López-Rubio¹

¹Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35. 29071 Málaga, Spain. ezeqlr@lcc.uma.es

Neural Networks : the Official Journal of the International Neural Network Society

|May 30, 2009

Summary

This summary is machine-generated.

This study introduces a robust Self-Organizing Map using Student-t distributions, improving outlier handling. The new model enhances performance in adaptive filtering and classification tasks.

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Area of Science:

Machine Learning
Artificial Intelligence
Data Science

Background:

The original Kohonen's Self-Organizing Map (SOM) has been extended to include probability distributions, typically Gaussian mixtures.
Existing models may lack robustness against outliers in data.

Purpose of the Study:

To present a novel Self-Organizing Map model based on a mixture of multivariate Student-t components.
To enhance the robustness of SOMs against outliers and include Gaussian mixtures as a limiting case.

Main Methods:

The proposed model utilizes a mixture of multivariate Student-t distributions within a stochastic approximation framework.
The 'degrees of freedom' parameter for each mixture component is estimated during the learning procedure, eliminating manual tuning.
The model's behavior with outliers and its performance in adaptive filtering and classification were evaluated experimentally.

Main Results:

The Student-t mixture-based SOM demonstrates improved robustness in the presence of outliers compared to Gaussian-based models.
The model effectively handles adaptive filtering and classification problems.
The 'degrees of freedom' parameter is automatically estimated, simplifying the model's application.

Conclusions:

The proposed Self-Organizing Map based on Student-t mixtures offers a more robust alternative to Gaussian-based SOMs, particularly for datasets with outliers.
The automatic estimation of the 'degrees of freedom' parameter enhances usability.
The model shows promise for applications in adaptive filtering and classification.