Abstract
In this chapter and the associated appendix present a third class of blind source separation and blind mixture identification methods intended for linear-quadratic mixtures (including their bilinear and purely quadratic restricted versions), namely methods based on sparse component analysis. Within this framework, the first type of methods measures sparsity by means of the L0 pseudo-norm, whereas the second type of methods exploits adjacent “single-source” data samples, that is, samples in which only one source is active, i.e., non-zero.
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Deville, Y., Tomazeli Duarte, L., Hosseini, S. (2021). Sparse Component Analysis Methods. In: Nonlinear Blind Source Separation and Blind Mixture Identification. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-64977-7_6
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DOI: https://doi.org/10.1007/978-3-030-64977-7_6
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