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Oja's neuron is extended to find the dominant eigenvalue alongside the computation of the dominant eigenvector. This is achieved through a stochastic gradient descent learning rule that computes the second moment of the neuron output. The effectiveness of this family of learning rules is further demonstrated in a network that verifies the law of total variance. The inputs are generated by a doubly...
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