Projective noise reduction with dynamic neighborhood selection

In recent years, several methods of noise reduction have been devised and applied to chaotic time series. Among them, projective methods have been particularly effective. We explain the non-orthogonal projective approach originally suggested by Grassberger et al. (1993) from a new point of view. We further discuss the extent to which a dynamic neighborhood selection improves the noise reduction results