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Characterisation of Signal Modality: Exploiting Signal Nonlinearity in Machine Learning and Signal Processing

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Abstract

A novel method for online tracking of the changes in the nonlinearity within both real-domain and complex–valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach based on a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. Implementations for tracking nonlinearity in general and then more specifically sparsity are illustrated on both benchmark and real world data. It is also shown that by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion.

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Notes

  1. The DVV method is a test for signal nonlinearity. For more detail, see [5, 10].

  2. The wind data with speed v and direction φ were made complex as v = ve iφ [11].

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Acknowledgements

We would like to thank Prof. Kazuyuki Aihara, Institute of Industrial Science, University of Tokyo, Japan for providing the wind data.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Imperial College London, London, UK

    Beth Jelfs, Soroush Javidi, Phebe Vayanos & Danilo Mandic

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  1. Beth Jelfs
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  2. Soroush Javidi
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  3. Phebe Vayanos
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  4. Danilo Mandic
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Correspondence to Beth Jelfs.

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Jelfs, B., Javidi, S., Vayanos, P. et al. Characterisation of Signal Modality: Exploiting Signal Nonlinearity in Machine Learning and Signal Processing. J Sign Process Syst 61, 105–115 (2010). https://doi.org/10.1007/s11265-009-0358-z

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  • DOI: https://doi.org/10.1007/s11265-009-0358-z

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