Oja's unsupervised learning is simply a procedure for plain Hebbian learning with constrained weight vector growth. This procedure adds a weight decay proportional to the output squared. Oja's rule finds a unit weight vector which maximizes the mean square output. For zero mean data this is equivalent to principal component analysis.
