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We present a sparse spectral correspondence method for deformable shape analysis. Our method exploits randomized sampling for sparse shape representation. By choosing a random subset of points that preserve key properties of the entire data set, it allows one to run algorithms efficiently on a small sample. First we implement random row sampling of an undirected weighted graph matrix by “Lewis...
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