A Variational Principle for Model-based Morphing

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Lawrence Saul, Michael Jordan


Given a multidimensional data set and a model of its density, we consider how to define the optimal interpolation between two points. This is done by assigning a cost to each path through space, based on two competing goals-one to interpolate through regions of high density, the other to minimize arc length. From this path functional, we derive the Euler-Lagrange equations for extremal motionj given two points, the desired interpolation is found by solv(cid:173) ing a boundary value problem. We show that this interpolation can be done efficiently, in high dimensions, for Gaussian, Dirichlet, and mixture models.