Chapter 8: From interpolation to fuzzy regression
Abstract
The innovative technique discussed here does much more than regression. It is useful in signal processing, in particular spatial filtering and smoothing. Initially designed using hyperplanes, the original version can be confused with support vector machines or support vector regression. However, the closest analogy is fuzzy regression. A weighted version based on splines makes it somewhat related to nearest neighbor or inverse distance interpolation, and highly nonlinear. In the end, it is a kriging-like spatial regression, with many potential applications, ranging from compression to signal enhancement and prediction. It comes with confidence intervals for the predicted values, despite ...
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