- Conformal ISOMAP
- relax the convex manifold assumption by preserving manifold orientation instead of geodesic distance.
- C ISOMAP
- allow for magnifying the regions of high density but shrinking the regions of low density of data points in manifold.
- Incremental ISOMAP
- allow for online ISOMAP learning by embedded points one by one instead of training in a batch manner.
- Landmark ISOMAP
- overcome high computational burden in learning by using landmarks, only a subset of representative data.
- Robust ISOMAP
- replace Dijkstra path-based geodesic distance estimates with parallel transport unfolding approximation for robustness to noise, a fundamental weakness of ISOMAP.