Manifold learning algorithm based on weighted landmark points
Wenbo Xu · Computer Engineering and Applications Journal · 2007
Several algorithms have been proposed to analyze the structure of high dimensional data based on the notion of manifold learning.Isomap is a representative nonlinear dimensionality reduction algorithm,which can discover low dimensional manifolds from high dimensional data.Isomap is simple but time-consuming.To speed up Isomap,L-Isomap,which uses landmark points,is proposed.But how to select landmarks is an open problem.In this paper,presents an extension of Isomap,namely WL-Isomap,which assigns data point variant weight according to the distance between it and its neighbors.Point with a higher weight has a lager probability to be selected as a landmark point.Experimental results show that WL-Isomap is more stable than L-Isomap and outper-forms L-Isomap especially when the number of landmark points is quite small.