Abstract
In this paper multilevel wavelet analysis is performed for high dimensional mass spectrometry data. A set of wavelet approximation coefficients at different scale is used to characterize the features of mass spectrometry data. Approximation coefficients compress mass spectrometry data and act as “fingerprint” of mass spectrometry data. Support vector machine is used to classify the different tissue based on these wavelet features. 2 and 3 fold cross validation experiments are performed on 2 datasets based on approximation coefficients at 1st, 2nd and 3rd level decomposition respectively. A highly competitive accuracy in comparison to the best performance of other kinds of classification models is achieved.
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Liu, Y. (2007). Dimensionality Reduction for Mass Spectrometry Data. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_20
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DOI: https://doi.org/10.1007/978-3-540-73871-8_20
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