Canonical-Correlation-Based Fast Feature Selection for Structural Health Monitoring

TL;DR

This paper introduces a rapid feature selection algorithm based on canonical correlation for structural health monitoring, demonstrating its efficiency and effectiveness on synthetic and real data, suitable for real-time and resource-constrained environments.

Contribution

It presents a novel fast feature selection method leveraging canonical correlation, optimized for quick computation in structural health monitoring applications.

Findings

Significantly faster feature selection compared to existing methods.

Effective in both classification and regression tasks.

Suitable for real-world SHM scenarios with environmental variability.

Abstract

Feature selection refers to the process of selecting useful features for machine learning tasks, and it is also a key step for structural health monitoring (SHM). This paper proposes a fast feature selection algorithm by efficiently computing the sum of squared canonical correlation coefficients between monitored features and target variables of interest in greedy search. The proposed algorithm is applied to both synthetic and real datasets to illustrate its advantages in terms of computational speed, general classification and regression tasks, as well as damage-sensitive feature selection tasks. Furthermore, the performance of the proposed algorithm is evaluated under varying environmental conditions and on an edge computing device to investigate its applicability in real-world SHM scenarios. The results show that the proposed algorithm can successfully select useful features with extraordinarily fast computational speed, which implies that the proposed algorithm has great potential where features need to be selected and updated online frequently, or where devices have limited computing capability.