Oja's rule

Oja's rule is an unsupervised learning algorithm used in the field of neural networks and machine learning, particularly in the context of learning vector representations. It is a type of Hebbian learning rule, which is based on the principle that neurons that fire together, wire together. Oja's rule is specifically designed to allow a neural network to learn the principal components of the input data, effectively performing a form of principal component analysis (PCA).