Unsupervised Learning

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Up until now we have discussed how to train nets given a training set of input and target values. The target value is often called the teacher signal because it represents the "right answer". i.e. what the output of the net should be. Training with a teacher signal is called supervised learning.

We can also train nets on inputs where there is no teacher signal. The purpose might be to

• discover underlying structure of the data
• encode the data
• compress the data
• transform the data

This kind of learning is called unsupervised learning because there is no explicit teacher signal.

Examples of unsupervised learning

• hebbian learning
  w(t+1) = w(t) + m y(t) x(t)
  This moves w toward inifinity in the direction of the eigevector with largest eigenvalue of the correlation matrix
  A more stable version is Oja's rule
  w(t+1) = w(t) + m (x(t) - y(t) w(t) ) y(t)
• principal component analysis
• competitive learning
• vector quantization

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