Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 62, Issue 11, Pages 2879-2893Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2014.2313528
Keywords
Multiresolution classification; semi-supervised learning; discrete signal processing on graphs; adaptive graph filter; indirect bridge structural health monitoring
Categories
Funding
- NSF [1130616, 1017278]
- CMU Carnegie Institute of Technology Infrastructure Award
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1130616] Funding Source: National Science Foundation
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We present a multiresolution classification framework with semi-supervised learning on graphs with application to the indirect bridge structural health monitoring. Classification in real-world applications faces two main challenges: reliable features can be hard to extract and few labeled signals are available for training. We propose a novel classification framework to address these problems: we use a multiresolution framework to deal with nonstationarities in the signals and extract features in each localized time-frequency region and semi-supervised learning to train on both labeled and unlabeled signals. We further propose an adaptive graph filter for semi-supervised classification that allows for classifying unlabeled as well as unseen signals and for correcting mislabeled signals. We validate the proposed framework on indirect bridge structural health monitoring and show that it performs significantly better than previous approaches.
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