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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Interpretable Sensor Change Detection via Conditional Cauchy-Schwarz Divergence.

Wenyu Wang¹, Yuan Shen², Yao Ni³

¹Department of Statistical Science, University College London, London WC1E 6BT, UK.

Sensors (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

This study introduces an interpretable Cauchy-Schwarz divergence method for detecting changes in multivariate sensor networks. It offers reliable change detection with sensor-level explanations for industrial and IoT systems.

Keywords:

Cauchy–Schwarz divergence change point detection industrial process monitoring interpretability sensor networks

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Area of Science:

Data Science
Machine Learning
Signal Processing

Background:

Multivariate sensor networks are crucial for monitoring complex systems.
Existing change-point detection methods often lack interpretability, limiting their use in critical applications.

Purpose of the Study:

To develop an interpretable framework for detecting distributional changes in multivariate sensor networks.
To provide sensor-level explanations for detected changes.

Main Methods:

The proposed method extends Cauchy-Schwarz (CS) divergence to conditional distributions.
It detects distributional shifts by analyzing changes in sensor-wise conditional relationships.

Main Results:

The framework achieves competitive or superior detection performance on synthetic and real-world data.
It provides fine-grained interpretability for practical sensor monitoring.

Conclusions:

The CS divergence-based method offers a reliable and interpretable solution for change detection in sensor networks.
This approach enhances the practical utility of monitoring systems in safety-critical domains.