Change-Point Detection on Hierarchical Circadian Models

Understand

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.