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Self-Organizing Feature Map (SOM)

  • SOMs, also called topological ordered maps, or Kohonen Self-Organizing Feature Map (KSOMs)
  • It maps all the points in a high-dimensional source space into a 2 to 3-d target space, s.t., the distance and proximity relationship (i.e., topology) are preserved as much as possible
  • Similar to k-means: cluster centers tend to lie in a low-dimensional manifold in the feature space
  • Clustering is performed by having several units competing for the current object
    • The unit whose weight vector is closest to the current object wins
    • The winner and its neighbors learn by having their weights adjusted
  • SOMs are believed to resemble processing that can occur in the brain
  • Useful for visualizing high-dimensional data in 2- or 3-D space

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