Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ACM Computing Surveys (CSUR)
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Feature Extraction and Selection from Vibration Measurements for Structural Health Monitoring
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Three-way analysis of structural health monitoring data
Neurocomputing
L1 norm based KPCA for novelty detection
Pattern Recognition
Pattern Recognition
Review: A review of novelty detection
Signal Processing
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The aim of Structural Health Monitoring (SHM) is to detect and identify damages in man-made structures such as bridges by monitoring features derived from vibration data. A usual approach is to deal with vibration measurements, obtained by acceleration sensors during the service life of the structure. In this case, only normal data from healthy operation are available, so damage detection becomes a novelty detection problem. However, when prior knowledge about the structure is limited, the set of candidate features that can be extracted from the set of sensors is large and dimensionality reduction of the input space can result in more precise and efficient novelty detectors. We assess the effect of linear, nonlinear, and random projection to low-dimensional spaces in novelty detection by means of probabilistic and nearest-neighbor methods. The methods are assessed with real-life data from a wooden bridge model, where structural damages are simulated with small added weights.