Optimized ISOMAP algorithm using similarity matrix

Dimension reduction techniques are used to obtain a reduced representation of the data that maintains the integrity of the original data. ISOMAP (Isometric Feature Mapping) is one of the dimension reduction techniques, which is a nonlinear generalization of Classical MDS (Multi-Dimensional Scaling) and works well both for real world and artificial data. It uses k-nearest neighbors concept for creating the neighborhood graph. In this paper, we have considered the similarity among data points as another approach for constructing the neighborhood graph, instead of using the concept of k-nearest neighbors.