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Unsupervised Learning Series —Exploring Self-Organizing Maps
Learn how Self-Organizing Maps work and why they are a useful unsupervised learning algorithm
Self-Organizing Maps (SOMs) are a type of unsupervised neural network utilized for clustering and visualization of high-dimensional data. SOMs are trained using a competitive learning algorithm, in which nodes (also known as neurons) in the network compete for the right to represent input data.
The SOM architecture consists of a 2D grid of nodes, where each node is associated with a weight vector that represents the means of the centroids in the SOM solution. The nodes are organized in such a way that nodes are organized around similar data points, producing a layer that represents the underlying data.
SOMs are commonly used for a wide array of tasks such as:
- data visualization
- anomaly detection
- feature extraction
- clustering
We can also visualize SOMs as the most simple neural network version for unsupervised learning!
While they seem confusing at first, Self-Organizing Maps (or Kohonen Maps, named after their inventor) are one interesting type of algorithm that is able to map the…
