Self-Organizing Map (SOM) Networks in Neural Networks

This article demonstrates how to classify data using Self-Organizing Map (SOM) networks, a type of neural network based on competitive learning. The core principle involves adjusting weight vectors through iterative training to map high-dimensional input data into lower-dimensional spaces (typically 2D grids). This mapping technique helps visualize data distribution patterns and plays a crucial role in data analysis and processing tasks. Key implementation aspects include: initializing network weights randomly, calculating Euclidean distances between input vectors and weight vectors, identifying the Best Matching Unit (BMU) through competitive learning, and updating weights using neighborhood functions that decay over training epochs. The algorithm employs a shrinking radius and learning rate to ensure convergence. We'll also explore practical applications in image analysis (feature clustering), speech recognition (phoneme mapping), and natural language processing (word vector visualization). Through this guide, readers will understand SOM's working mechanism - including the topological preservation property - and learn to implement it for real-world problems using libraries like MiniSom or TensorFlow, with code snippets illustrating distance calculations and weight update procedures.