To address the above outlined problems occurring to LLE when employed
with a high number of neighbors, we propose the usage of LLE with geodesic
distance (ISOLLE). More specically, the n nearest neighbors are searched with
respect to the geodesic distance. This metric has already been employed in other
methods for nonlinear dimensional data reduction such as Isomap [8], Curvi-
lIn this work we demonstrate that the employment of the geodesic distance
can lower the probability to create short circuits during the neighbors search,
thereby allowing for a more accurate dimensional reduction. Our approach to
investigate the performances of ISOLLE as compared to conventional LLE is