Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model
Algorithm 1
Autoencoder (AE) training algorithm.
Step 1: start
Step 2: given the set of training samples, the number of cells in the visible and hidden layers, the number of iterations, the learning rate, the initialized training parameter weight matrix W, the bias vectors b, c, and the canonical terms
Step 3: using the restricted Newton’s method algorithm, update the training parameters until the algorithm converges
