Self-organizing Maps

Self-organizing Maps

Self-organizing Maps (SOMs) are a type of artificial neural network (ANN) that are trained using unsupervised learning to produce a low-dimensional, discretized representation of the input space of the training samples, called a map. They are also known as Kohonen maps, named after Teuvo Kohonen, a Finnish professor who invented them.

What are Self-organizing Maps?

SOMs are a data visualization technique which reduce the dimensions of data through the use of self-organizing neural networks. The main characteristic of SOMs is that they maintain the topological properties of the input data. In other words, they provide a method of representing multidimensional data in much lower dimensions (usually two), while preserving the relative distances between different data points.

How do Self-organizing Maps work?

SOMs work by iteratively adjusting the ‘weights’ of the neurons in the network to match the input vectors. Each neuron in the network represents one cluster or class. The algorithm starts by initializing the weights randomly, then it selects a random vector from the input data and computes its Euclidean distance to the weights of each neuron. The neuron with the smallest distance (the Best Matching Unit or BMU) is selected, and its weights, as well as the weights of the neurons in its neighborhood, are adjusted to make them more similar to the input vector. This process is repeated for a large number of iterations.

Why use Self-organizing Maps?

SOMs are particularly useful for visualizing high-dimensional data. They can reveal correlations that are not easily identified in the raw data, and they can group similar data together, making it easier to identify patterns and trends. They are often used in exploratory data analysis, anomaly detection, and clustering.

Applications of Self-organizing Maps

SOMs have a wide range of applications in various fields. In data science, they are often used for clustering and visualization of high-dimensional data. In finance, they can be used to detect patterns and anomalies in transaction data. In bioinformatics, they can be used to cluster gene expression data. In image processing, they can be used for color quantization and image segmentation.

Limitations of Self-organizing Maps

While SOMs are a powerful tool for data visualization and clustering, they do have some limitations. The quality of the results can be sensitive to the choice of parameters, such as the size of the map and the learning rate. They also do not provide a probabilistic model of the data, which can be a disadvantage in some applications.

Further Reading

  • Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480.
  • Vesanto, J., & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on neural networks, 11(3), 586-600.
  • Artificial Neural Networks (ANNs)
  • Unsupervised Learning
  • Clustering
  • Data Visualization
  • Dimensionality Reduction
  • Euclidean Distance
  • Best Matching Unit (BMU)
  • Exploratory Data Analysis
  • Anomaly Detection
  • Bioinformatics
  • Image Processing
  • Color Quantization
  • Image Segmentation
  • Topological Properties
  • Weight Adjustment
  • Iterative Learning
  • Data Correlation
  • Pattern Recognition
  • Trend Identification
  • Transaction Data
  • Gene Expression Data
  • Learning Rate
  • Map Size
  • Probabilistic Model
  • Parameter Sensitivity
  • Data Science
  • Finance
  • Image Processing
  • Bioinformatics