What is the difference between Kohonen network and MLP algorithms in C++?

Table of Contents

• Introduction
• Key Differences between Kohonen Network and MLP
  • 1. Learning Mechanism
  • 2. Architecture
  • 3. Output and Use Cases
• Example of Kohonen Network vs MLP in C++
  • Kohonen Network Example
  • MLP Example
• Conclusion

Introduction

Both Kohonen Networks (Self-Organizing Maps) and Multi-Layer Perceptrons (MLP) are popular neural network models, but they serve different purposes and are based on different principles. The Kohonen network is primarily used for unsupervised learning and data clustering, while the MLP is a model for supervised learning used for tasks like classification and regression. In this article, we will explore the differences between these two models in terms of architecture, learning mechanism, and use cases in C++.

Key Differences between Kohonen Network and MLP

1. Learning Mechanism

• Kohonen Network (Self-Organizing Map)
  • Unsupervised Learning: Kohonen networks, or Self-Organizing Maps (SOM), use unsupervised learning. They map high-dimensional input data onto a lower-dimensional grid (typically 2D), where similar data points cluster together.
  • Competitive Learning: During training, only the neuron (or node) closest to the input data, known as the Best Matching Unit (BMU), updates its weights. Neurons close to the BMU also update their weights in a process called neighborhood updating.
• MLP (Multi-Layer Perceptron)
  • Supervised Learning: MLPs are trained using labeled datasets, where the goal is to learn a mapping from inputs to specific outputs, minimizing the error between predicted and target outputs.
  • Backpropagation and Gradient Descent: MLPs are typically trained using the backpropagation algorithm and gradient descent. The errors from the output layer are propagated backward to update the weights in all layers.

2. Architecture

• Kohonen Network
  • Single Layer of Neurons: A Kohonen network consists of a single layer of neurons organized in a grid, which forms a topological map.
  • No Activation Function: The neurons in a Kohonen network do not use non-linear activation functions like sigmoid or ReLU. Instead, they compete to become the BMU based on the input's proximity.
• MLP
  • Multiple Layers: MLP has an input layer, one or more hidden layers, and an output layer. Each layer is fully connected to the next.
  • Activation Functions: Neurons in the hidden layers and output layers often use non-linear activation functions like sigmoid, tanh, or ReLU to model complex patterns.

3. Output and Use Cases

• Kohonen Network
  • Clustering and Visualization: Kohonen networks are used for clustering data and reducing dimensionality. They are particularly useful for data visualization tasks like mapping complex datasets into a 2D space while preserving the topological relationships between data points.
  • Unsupervised Feature Learning: Kohonen networks are ideal for unsupervised feature extraction and pattern recognition.
• MLP
  • Classification and Regression: MLPs are applied to tasks like classification, where inputs are assigned to different classes, and regression, where the model predicts continuous values based on input data.
  • Complex Problem Solving: MLPs are suited for more complex tasks where input-output mappings are required.

Example of Kohonen Network vs MLP in C++

Kohonen Network Example

Here is a basic outline of a Kohonen network implementation in C++:

MLP Example

For comparison, here’s a simplified MLP implementation in C++:

Conclusion

The Kohonen Network (Self-Organizing Map) and MLP serve distinct purposes in machine learning. The Kohonen network is used for unsupervised learning and clustering, relying on competitive learning, while the MLP is a supervised learning model used for classification and regression tasks, employing backpropagation. Each model excels in different types of tasks, making their usage context-specific in the realm of neural networks.