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Learning in Riemannian orbifolds is motivated by existing machine learning algorithms that directly operate on finite combinatorial structures such as point patterns, trees, and graphs.
Adaptive switching circuits
B. Widrow and M.E. Hoff · 1960
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Stochastic generalized-differentiable functions in the problem of nonconvex nonsmooth stochastic optimization
V.I. Norkin · 1986
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An eigendecomposition approach to weighted graph matching problems
S. Umeyama · 1988
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Riemannian geometry of orbifolds
J.E. Borzellino · 1992
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A linear programming approach for the weighted graph matching problem
H.A. Almohamad and S.O. Duffuaa · 1993
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A graduated assignment algorithm for graph matching
S. Gold and A. Rangarajan · 1996
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Learning with preknowledge: Clustering with point and graph matching distance measures
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On a relation between graph edit distance and maximum common subgraph
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Stochastic generalized gradient method for nonconvex nonsmooth stochastic optimization
Y. M. Ermoliev and V.I. Norkin · 1998
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Graph-based k-means clustering: A comparison of the set median versus the generalized median graph
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