Abstract
Whereas the previous chapters of this book are focused on blind source separation and blind mixture identification methods intended for linear-quadratic mixtures (including their restricted versions), we here extend this overview to the main trends that may be observed in methods suited to more general mixtures, namely higher-order polynomial ones, which have been addressed in a few reported investigations, e.g., to handle multiple (beyond second-order) reflections in spectral unmixing applications. We also discuss configurations where the source signals have variability (called spectral or intraclass variability by the remote sensing community) or when the considered nonlinear mixtures are not memoryless. We then conclude about all these types of mixtures and associated methods, as well as their applications.
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