Manifold Learning by Graduated Optimization
Michael S. Gashler, Dan A. Ventura, Tony R. Martinez · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2011
We present an algorithm for manifold learning called manifold sculpting , which utilizes graduated optimization to seek an accurate manifold embedding. An empirical analysis across a wide range of manifold problems indicates that manifold sculpting yields more accurate results than a number of existing algorithms, including Isomap, locally linear embedding (LLE), Hessian LLE (HLLE), and landmark maximum variance unfolding (L-MVU), and is significantly more efficient than HLLE and L-MVU. Manifold sculpting also has the ability to benefit from prior knowledge about expected results.