Proceedings of the Royal Society of London A, Mathematical, Physical and Engineering Sciences vol:464 issue:2093 pages:1141-1160
The need for the characterization of real-world signals in terms of their linear, nonlinear,
deterministic and stochastic nature is highlighted and a novel framework for signal
modality characterization is presented. A comprehensive analysis of signal nonlinearity
characterization methods is provided, and based upon local predictability in phase space,
a new criterion for qualitative performance assessment in machine learning is introduced.
This is achieved based on a simultaneous assessment of nonlinearity and uncertainty
within a real-world signal. Next, for a given embedding dimension, based on the target
variance of delay vectors, a novel framework for heterogeneous data fusion is introduced.
The proposed signal modality characterization framework is verified by comprehensive
simulations and comparison against other established methods. Case studies covering a
range of machine learning applications support the analysis.