Abstract
This paper compares three unsupervised projection methods: Principal Component Analysis (PCA), which is linear, Self-Organizing Map (SOM) and Curvilinear Component Analysis (CCA), which are both nonlinear. Performance comparison of the three methods is made on a set of seismic data recorded on Stromboli that includes three classes of signals: explosion-quakes, landslides, and microtremors. The unsupervised analysis of the signals is able to discover the nature of the seismic events. Our analysis shows that the SOM algorithm discriminates better than CCA and PCA on the data under examination.
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Esposito, A.M., Scarpetta, S., Giudicepietro, F., Masiello, S., Pugliese, L., Esposito, A. (2006). Nonlinear Exploratory Data Analysis Applied to Seismic Signals. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_11
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DOI: https://doi.org/10.1007/11731177_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
Online ISBN: 978-3-540-33184-1
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