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Multi-Source Change-Point Detection over Local Observation Models
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DOI：10.1016/j.patcog.2022.109116.png)
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Abstract
En 中文
In this work, we address the problem of change-point detection (CPD) on high-dimensional, multi-source, and heterogeneous sequential data with missing values. We present a new CPD methodology based on local latent variable models and adaptive factorizations that enhances the fusion of multi-source obser-vations with different statistical data-type and face the problem of high dimensionality. Our motivation comes from behavioral change detection in healthcare measured by smartphone monitored data and Elec-tronic Health Records. Due to the high dimension of the observations and the differences in the relevance of each source information, other works fail in obtaining reliable estimates of the change-points location. This leads to methods that are not sensitive enough when dealing with interspersed changes of different intensity within the same sequence or partial missing components. Through the definition of local obser-vation models (LOMs), we transfer the local CP information to homogeneous latent spaces and propose several factorizations that weight the contribution of each source to the global CPD. With the presented methods we demonstrate a reduction in both the detection delay and the number of not-detected CPs, together with robustness against the presence of missing values on a synthetic dataset. We illustrate its application on real-world data from a smartphone-based monitored study and add explainability on the degree of each source contributing to the detection. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Keywords:
Change -point detection
Multi -source data
Heterogeneous data
Latent variable models
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