How do we quickly detect small solar flares in a large video stream generated by NASA satellites? How do we improve detection by efficient representation of high-dimensional data that is time-varying? Besides astronomical imaging, high-dimensional change-point detection also arises in many other applications including computer network intrusion detection, sensor networks, medical imaging, and epidemiology. In these problems, each dimension of the data is obtained by a sensor, and there are multiple sensors monitoring the emergence of a signal---an abrupt change in the distribution of the observations. The goal is to detect such a signal as soon as possible after it occurs, and make as few false alarms as possible.
Two key challenges in high-dimensional change-point detection are 1) how to extract useful statistics, 2) how to find an efficient representation of the data. Many high-dimensional data exhibit low-dimensional structures such as sparsity, or the data may lie on a low-dimensional manifold. The approach I take is to exploit these low-dimensional structures in change-point detection. I will describe a mixture procedure that exploits sparsity, and MOUSSE, an online algorithm for tracking the evolving data manifold and extracts efficient statistics for change-point detection.
More information about Yao Xie may be found at http://www2.isye.gatech.edu/~yxie77/