Semi-supervised multiresolution classification using adaptive graph filtering with application to indirect bridge structural health monitoring

Siheng Chen, Fernando Cerda, Piervincenzo Rizzo, Jacobo Bielak, James H. Garrett, Jelena Kovačević

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
Article number6778068
Pages (from-to)2879-2893
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number11
StatePublished - Jul 1 2014



  • Adaptive graph filter
  • Discrete signal processing on graphs
  • Indirect bridge structural health monitoring
  • Multiresolution classification
  • Semi-supervised learning

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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