Abstract
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.
This work was supported by the Swiss National Science Foundation (grant no.105211-107862).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ratle, F., Terrettaz, A.L., Kanevski, M., Esseiva, P., Ribaux, O.: Pattern analysis in illicit heroin seizures: a novel application of machine learning algorithms. In: Proc. of the 14th European Symposium on Artificial Neural Networks. d-side publi. (2006)
Guëniat, O., Esseiva, P.: Le Profilage de l’Hëroïne et de la Cocaïne, Presses polytechniques et universitaires romandes, Lausanne (2005)
Esseiva, P., Dujourdy, L., Anglada, F., Taroni, F., Margot, P.: A methodology for illicit drug intelligence perspective using large databases. Forensic Science International 132, 139–152 (2003)
Esseiva, P., Anglada, F., Dujourdy, L., Taroni, F., Margot, P., Du Pasquier, E., Dawson, M., Roux, C., Doble, P.: Chemical profiling and classification of illicit heroin by principal component analysis, calculation of inter sample correlation and artificial neural networks. Talanta 67, 360–367 (2005)
Madden, M.G., Ryder, A.G.: Machine Learning Methods for Quantitative Analysis of Raman Spectroscopy Data. In: Proceedings of the International Society for Optical Engineering (SPIE 2002), vol. 4876, pp. 1130–1139 (2002)
O’Connell, M.L., Howley, T., Ryder, A.G., Madden, M.G.: Classification of a target analyte in solid mixtures using principal component analysis, support vector machines, and Raman spectroscopy. In: Proceedings of the International Society for Optical Engineering (SPIE 2005), vol. 4876, pp. 340–350 (2005)
Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)
Jenkins, O.C., Matarić, M.J.: A spatio-temporal extension to isomap nonlinear dimension reduction. In: Proc. of the 21st International Conference on Machine Learning (2004)
Stork, D.G., Yom-Tov, E.: Computer Manual in MATLAB to accompany Pattern Classification. Wiley, Hoboken (2004)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, NewYork (2001)
Kruskal, J.B., Wish, M.: Multidimensional Scaling. SAGE Publications, Thousand Oaks (1978)
Bengio, Y., Paiement, J.F., Vincent, P., Delalleau, O., Le Roux, N., Ouimet, M.: Out-of-samples extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. Advances in Neural Information Processing Systems 16 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ratle, F., Terrettaz-Zufferey, AL., Kanevski, M., Esseiva, P., Ribaux, O. (2006). Learning Manifolds in Forensic Data. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_93
Download citation
DOI: https://doi.org/10.1007/11840930_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
eBook Packages: Computer ScienceComputer Science (R0)