where n is the number of training examples, x * i is the vector of inputs for the i-th training sample, xi is the output vector of the Autoencoder, and the difference is calculated as the mean squared error (MSE) of the samples, expressed in terms of vector norms. The global dataset accumulated across iterations is used for optimizing the Autoencoder.