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Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM).

B Zhang¹, M Fu, H Yan

¹Department of Electrical and Computer Engineering, University of Newcastle, NSW 2308, Australia.

IEEE Transactions on Neural Networks

|February 7, 2008

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We introduce a novel neural network approach for the adaptive-subspace self-organizing map (ASSOM) for handwritten digit recognition. This method achieves high accuracy, demonstrating its effectiveness in pattern classification tasks.

Area of Science:

Artificial Intelligence
Machine Learning
Neural Networks

Background:

The adaptive-subspace self-organizing map (ASSOM) is an advanced technique in self-organizing map (SOM) computation.
Existing methods for ASSOM implementation may face challenges with numerical stability.

Purpose of the Study:

To propose a numerically stable method for realizing ASSOM using nonlinear autoencoder networks.
To apply the developed ASSOM model to a modular handwritten digit recognition system.

Main Methods:

Implementation of ASSOM via a neural learning algorithm within nonlinear autoencoder networks.
Development of a modular classification system using ten ASSOM modules, each dedicated to a digit class.
Classification based on comparing reconstruction errors from each module.

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Main Results:

The proposed ASSOM method demonstrates numerical stability.
The modular classification system achieved 99.3% accuracy on the training set.
The system attained over 97% classification accuracy on the testing set for handwritten digits.

Conclusions:

The neural learning algorithm effectively realizes ASSOM with enhanced numerical stability.
The modular ASSOM system is a promising approach for handwritten digit recognition.
The method shows high classification accuracy even with relatively small module sizes.