Model change detection with application to machine learning

Throughout this paper, we use lower case letters to denote scalars and vectors, and use upper case letters to denote random variablesand matrices. We consider the model change detection problem in the following setting. ABSTRACT Model change detection is studied, in which there are two sets of samples that are independently and identically distributed (i.i.d.) according to a pre-change probabilistic model with parameter θ,and a post-change model with parameter θ The goal is to detect whether the change in the model is significant, i.e., whether the difference between the prechange parameterand the post-change parameter ‖θ θ The problem is considered in a Neyman-Pearson setting, where the goal is to maximize the probability of detection under a false alarm constraint. Since the generalized likelihood ratio test (GLRT) is difficult to compute in this problem, we construct an empirical differencetest (EDT), which approximates the GLRT and has low computational complexity. Moreover, we provide an approximation method to set the threshold of the EDT to meet the false alarm constraint.