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Among the advantages of unsupervised learning is that it does not require labeled data, which means that there is usually more data available for learning.

Regular Hebbian learning leads to all neurons responding to the same input. One method to force neurons to specialize is competitive learning.

Competitive learning can be implemented in ANN by strong, constant inhibitory connections between competing neurons.

Simple competitive neural learning with constant inhibitory connections between competing neurons leads to grandmother-type cells.

Simple competitive neural learning with constant inhibitory connections between competing neurons produces a code that facilitates further processing.

Kohonen implies that neighborhood interaction in SOMs is what separates them from earlier, more bio-inspired attempts at input-driven self-organization, and what leads to computational tractability on the one hand and proper self-organization as found in natural brain maps on the other.

Kohonen states that online learning in SOMs is less safe and slower than batch learning.

Kohonen names normalization of input dimensions as a remedy for differences in scaling between these dimensions. He does not cite another paper of his (with colleagues) in which he presents a SOM that learns this scaling.

My SOM takes care of differences in scaling between input dimensions implicitly and weights input dimensions while Kangas et al.'s SOM only learns scaling.

Kohonen discusses some of the challenges involved in using SOMs for text clustering.

  • words have different importance depending on their absolute frequency,
  • some words occurring very rarely or very commonly must be discarded.

It would be really interesting to see whether SOMs for text clustering can be designed whose weight vectors code for different sets of words.

Kohonen groups applications of SOMs into

  • statistical methods
    • exploratory data analysis
    • statistical analysis in organization of texts
  • industrial analyzes, control, telecommunications
  • financial applications