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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Learning Manifolds in Forensic Data
verfasst von : Frédéric Ratle, Anne-Laure Terrettaz-Zufferey, Mikhail Kanevski, Pierre Esseiva, Olivier Ribaux
Erschienen in: Artificial Neural Networks – ICANN 2006
Verlag: Springer Berlin Heidelberg
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Chemical data related to illicit cocaine seizures is analyzed using linear and nonlinear dimensionality reduction methods. The goal is to find relevant features that could guide the data analysis process in chemical drug profiling, a recent field in the crime mapping community. The data has been collected using gas chromatography analysis. Several methods are tested: PCA, kernel PCA, isomap, spatio-temporal isomap and locally linear embedding. ST-isomap is used to detect a potential time-dependent nonlinear manifold, the data being sequential. Results show that the presence of a simple nonlinear manifold in the data is very likely and that this manifold cannot be detected by a linear PCA. The presence of temporal regularities is also observed with ST-isomap. Kernel PCA and isomap perform better than the other methods, and kernel PCA is more robust than isomap when introducing random perturbations in the dataset.