An improved set covering problem for Isomap supervised landmark selection ?
A novel algorithm for Isomap supervised landmark selection is presented. It relies on a weighted set covering problem solved via Lagrangian relaxation with subgradient optimization. The proposed technique empirically dominated prominent competing methods. It also emerged as a valuable alternative to Isomap for the projection of large labeled data sets.
set covering problem landmark selection nonlinear dimensionality reduction isometric feature mapping classification