Spectral Kernels for Probabilistic Analysis and Clustering of Shapes
IPMI, pp. 67-79, 2017.
We propose a framework for probabilistic shape clustering based on kernel-space embeddings derived from spectral signatures. Our root motivation is to investigate practical yet principled clustering schemes that rely on geometrical invariants of shapes rather than explicit registration. To that end we revisit the use of the Laplacian spec...More
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