A new paradigm for signal fitting is proposed. Unlike the customary approach in which fixed basis functions are used to represent the signal, the proposed method employs random basis functions. The advantage is an increase in robustness, leading to an overall decrease in modeling error. It also provides a new intepretation on the choice of regularization weightings for such applications as classification, spectral analysis, and adaptive beamforming.
['Steven M. Kay']
Signal Fitting With Uncertain Basis Functions