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Isometric feature mapping (ISOMAP) has two computational bottlenecks. The first is calculating the N×N graph distance matrix DN. Using Floyd΄s algorithm, this is O(N3); this can be improved to O(kN2 log N) by implementing Dijkstra΄s algorithm. The second bottleneck is the MDS eigenvalue calculation, which involves a full N×N matrix and has complexity O(N3). In this paper, we address both of these inefficiencies by a greedy approximation algorithm of minimum set coverage (MSC). The algorithm learns a minimum subset of overlapping neighborhoods for high dimensional data that lies on or near a low dimensional manifold. The new framework leads to order-of-magnitude reductions in computation time and makes it possible to study much larger problems in manifold learning. [ABSTRACT FROM AUTHOR]