Abstract-We study sequential change-point detection using sketches (or linear projections) of the high-dimensional data vectors, and present a new sketching procedure, which is based on the generalized likelihood ratio statistic. We derive theoretical approximations to two fundamental performance metrics for the sketching procedures: the average run length (ARL) and the expected detection delay (EDD), and these approximations are shown to be highly accurate by numerical simulations. We also analyze the ratio of EDD between the sketching procedure and a procedure using the original data, when the sketching matrix A is a random Gaussian matrix and a sparse 0-1 matrix (in particular, a expander graph), respectively. Finally, numerical examples demonstrate that the sketching procedure can approach the performance of a procedure that uses the original data, even when the post-change mean vector is not sparse.
Xie, Wang, and Thompson REF studied detecting change-points in high-dimensional signal vectors using statistics based on the generalized likelihood ratio.
Sketching for sequential change-point detection
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Computer Science Mathematics