The OJA neural network, also known as Oja's rule neural network, is a type of artificial neural network used for unsupervised learning in dimension reduction and principal component analysis problems.
It was developed by Finnish mathematician Erkki Oja in 1982 and is based on a learning algorithm that allows the neural network to find the principal directions of the input features and reduce the dimension of the data. Compared to other dimension reduction methods, the OJA neural network is better able to handle highly correlated and non-linear data.
The operation of the OJA neural network is based on adjusting the synaptic weights of the network so that the output neuron responds selectively to specific input patterns. This is achieved by iteratively calculating the synaptic weights to maximise the correlation between the inputs and the output neuron.
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