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

Table of Contents

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

Introduction

The Kohonen Network (also known as Self-Organizing Map or SOM) and Multi-Layer Perceptron (MLP) are both neural network models but they serve very different purposes. Kohonen networks are designed for unsupervised learning, especially for clustering and dimensionality reduction, while MLPs are primarily used in supervised learning tasks, such as classification and regression. In this article, we'll compare these two models in terms of their learning mechanisms, architectures, and typical implementations in the C programming language.

Differences between Kohonen Network and MLP Algorithms in C

1. Learning Mechanism

• Kohonen Network (Self-Organizing Map)
  • Unsupervised Learning: Kohonen networks employ unsupervised learning where no labels are required. The network organizes input data into clusters or patterns based on similarity, creating a lower-dimensional representation.
  • Competitive Learning: During training, only the neuron closest to the input data, called the Best Matching Unit (BMU), and its neighboring neurons are updated. This competitive learning process helps in clustering data.
• MLP (Multi-Layer Perceptron)
  • Supervised Learning: MLPs are trained using labeled data, where each input is mapped to a corresponding output (class label or continuous value). MLPs aim to minimize error between predicted and target outputs through learning.
  • Backpropagation and Gradient Descent: MLPs are typically trained using backpropagation with gradient descent to adjust weights across multiple layers of neurons. Errors are propagated backward from the output to the input layers.

2. Architecture

• Kohonen Network
  • Single Layer of Neurons: A Kohonen network consists of a single layer of neurons arranged in a grid. Neurons map input data to this grid based on proximity.
  • No Activation Function: Unlike MLPs, the Kohonen network doesn't use activation functions like ReLU or sigmoid. The training is based on distance metrics (e.g., Euclidean distance) to find the Best Matching Unit (BMU).
• MLP
  • Multiple Layers: MLPs have multiple layers of neurons, including an input layer, one or more hidden layers, and an output layer. Each neuron in a layer is connected to all neurons in the subsequent layer (fully connected).
  • Activation Functions: MLPs use activation functions such as sigmoid, ReLU, or tanh in the hidden layers to model complex, non-linear relationships between input and output.

3. Use Cases and Output

• Kohonen Network
  • Data Clustering and Visualization: Kohonen networks are well-suited for clustering large datasets and reducing dimensionality. They are commonly used for tasks like feature extraction and pattern recognition.
  • Unsupervised Feature Mapping: The output is a map of clusters where similar input patterns are grouped together, which can be used for data visualization.
• MLP
  • Classification and Regression: MLPs are used for solving supervised learning problems like classification (e.g., image or text classification) and regression (e.g., predicting numerical values).
  • Complex Decision Boundaries: MLPs are excellent for modeling complex relationships and decision boundaries in data.

Example of Kohonen Network vs MLP in C

Kohonen Network Example in C

Below is a simplified example of a Kohonen network in C. This implementation focuses on finding the Best Matching Unit (BMU) and updating its weights.

MLP Example in C

Here is a simplified version of MLP in C with a forward pass implementation:

Conclusion

The Kohonen Network (Self-Organizing Map) and MLP differ fundamentally in terms of their learning methods, architectures, and use cases. The Kohonen network is an unsupervised learning model primarily used for clustering and dimensionality reduction, while MLP is a supervised learning model applied to tasks such as classification and regression. Both models have unique strengths, and their applicability depends on the specific task and dataset at hand.