Change-Point Detection on Hierarchical Circadian Models
This work addresses the problem of misidentifying drifts as changes in high-dimensional periodic data, with incremental improvements for applications like relapse detection in psychiatric patients using smartphones.
The paper tackles change-point detection in high-dimensional, heterogeneous, and periodic data by proposing a hierarchical model with latent variable representation and non-stationary periodic covariance functions, validated on synthetic and real smartphone data for detecting behavioral changes in psychiatric patients.
This paper addresses the problem of change-point detection on 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 on 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 fitted 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.