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Sparse Projections of Medical Images onto Manifolds
[article]
2013
arXiv
pre-print
Manifold learning has been successfully applied to a variety of medical imaging problems. Its use in real-time applications requires fast projection onto the low-dimensional space. To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold. Commonly used approaches such as the Nyström extension and kernel ridge regression require using all training points. We propose an interpolation function that
arXiv:1303.5508v2
fatcat:xuefvzjegfgxbfo77fj3326kkq