Abstract
This work proposes translation-invariant features based on a wavelet transform that are used to classify time series as containing either relevant signals or noisy background. Due to the translation-invariant property, signals appearing at arbitrary locations in time have similar representations in feature space. Classification is carried out by a condensed \(k\)-Nearest-Neighbors classifier trained on these features, i.e. the training set is reduced for faster classification. This reduction is conducted by a \(k\)-means clustering of the original training set and using the obtained cluster centers as a new training set. The coreset-technique BICO is employed to accelerate this initial clustering for big datasets. The resulting feature extraction and classification pipeline is applied successfully in the context of biological virus detection. Data from Plasmon Assisted Microscopy of Nano-size Objects (PAMONO) is classified, achieving accuracy \(0.999\) for the most important classification task.
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Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876. URL: http://sfb876.tu-dortmund.de/
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Siedhoff, D., Fichtenberger, H., Libuschewski, P., Weichert, F., Sohler, C., Müller, H. (2014). Signal/Background Classification of Time Series for Biological Virus Detection. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_31
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DOI: https://doi.org/10.1007/978-3-319-11752-2_31
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