Article ID: | iaor20091468 |
Country: | United Kingdom |
Volume: | 464 |
Issue: | 2093 |
Start Page Number: | 1141 |
End Page Number: | 1160 |
Publication Date: | May 2008 |
Journal: | Proc Royal Soc A: Mathematical, Physical & Engineering Sciences |
Authors: | Chen M., Mandic D.P., Gautama T., Hulle M.M. Van, Constantinides A. |
Keywords: | artificial intelligence |
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.