Self-organizing Map





A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data.

How to use:

  1. Upload an image - it will be used as the training data
  2. Adjust learning parameters to control how the map adapts
  3. Watch as the neural network organizes itself to match the image

Parameters explained:

  • Initial Range: How far neurons influence their neighbors initially
  • Learning Rate: How quickly neurons adapt to the data
  • Decay Rates: How these values decrease over time
  • Weight factors: Balance between position and color matching

The SOM is a type of artificial neural network that creates a 2D map of high-dimensional input. It's used for dimensionality reduction, visualization, and clustering. Watch as the network self-organizes to represent the color and spatial structure of your image.