Estimation of symmetric chi-square divergence for point processes

This paper addresses the estimation of symmetric χ2 -divergence between two point processes. We propose a novel approach by, first, mapping the space of spike trains in an appropriate functional space, and then, estimating the divergence in this functional space using a least square regression approach. We compare the proposed approach with other available methods on simulated data, and discuss its pros and cons.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Il Park, Sohan Seth, Murali Rao, José Carlos Príncipe
• Keywords: Appropriate Functional Space, Functional Space, ICASSP 2011, Signal Processing, Square Regression Approach