Differential learning and random walk model

Abstract

This paper presents a learning algorithm for differential decorrelation, the goal of which is to find a linear transform that minimizes the concurrent change of associated output nodes. First the algorithm is derived from the minimization of the objective function which measures the differential correlation. Then we show that the differential decorrelation learning algorithm can also be derived in the framework of maximum likelihood estimation of a linear generative model with assuming a random walk model for latent variables. Algorithm derivation and local stability analysis are given with a simple numerical example.