Landmark point selection using clustering for data classification

This paper proposes a clustering landmark selection technique for Landmark Isomap (L-Isomap). L-Isomap randomly selects a set of points called landmark points from the data set, for computing the distance from the selected landmark points to all other non landmark points. Selection of the landmark points is crucial in proper representation of the data. The number of landmark points selected and the location of these points will be dependent on the data properties. The proposed method when compared with random L-Isomap and Isomap, performs well for different landmark points for different databases.