Discriminative recurring signal detection and localization

Recognizing and localizing a recurring pattern is a problem with a variety of applications such as classification and localization of home appliances from their activation signals and estimating the relative alignment between records of a natural repetitive electrocardiography (ECG) signals in Bio-medical data. Most common approaches for recognizing a recurring pattern are generative and focus on discovering and capturing the characteristics of the recurring pattern. One limitation of such generative approaches is that they are more sensitive to variations of the recurring signal. In this paper, we present a discriminative approach for finding a recurring pattern and localizing it within a collection of signals. We evaluate and compare our method to a generative approach [1] on both synthetic data and real-world home appliance data.