Analytical feature extraction and spectral summation

We propose a formalism for analysing multilayer perceptron (MLP) networks as propagations of binary transitions along excitatory and inhibitory sensitised paths. By characterising a Boolean function as sets of detected transitions, we produce a spectral summation and construct a network from the derived weight constraints. We build hidden node feature detectors by incorporating k-monotonicity checks in the partitioning step of a constructive algorithm. Propagation constraints are also used in an MLP network using gradient descent learning to limit hyperplane movement in weight space. Results for a pattern classification task represented as a binary-to-binary mapping show improved convergence and generalisation performance