This paper addresses the problem of change-point detection in sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to the dimensionality problem, when the time between change points is of the order of the dimension of the model parameters, drifts in the underlying distribution can be misidentified as changes. To overcome this limitation, we assume that the observations lie in a lower-dimensional manifold that admits a latent variable representation. In particular, we propose a hierarchical model that is computationally feasible, widely applicable to heterogeneous data and robust to missing instances. Additionally, the observations’ periodic dependencies are captured by non-stationary periodic covariance functions. The proposed technique is particularly well suited to (and motivated by) the problem of detecting changes in human behavior using smartphones and its application to relapse detection in psychiatric patients. Finally, we validate the technique on synthetic examples and we demonstrate its utility in the detection of behavioral changes using real data acquired by smartphones.

本文解决了高维和非均质观测序列中的变化点检测问题，这些序列也具有周期性的时间结构。由于维数问题，当变化点之间的时间约为模型参数维数时，基础分布中的漂移会被错误地识别为变化。为了克服此限制，我们假设观测值位于允许潜在变量表示的较低维流形中。特别是，我们提出了一种层次模型，该模型在计算上是可行的，广泛适用于异构数据，并且对于丢失的实例具有鲁棒性。此外，观测值的周期性依存关系是由非平稳周期性协方差函数捕获的。所提出的技术特别适用于（并受其启发）使用智能手机检测人类行为变化的问题，并将其应用于精神病患者的复发检测。最后，我们在合成示例上验证了该技术，并展示了其在使用智能手机获取的真实数据检测行为变化中的效用。