Parametric Regression on the Grassmannian
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In [Paper, Algorithms 1 and 2] we need the gradient of the residuals dg(Y(ri),Yi) (here, Y(ri) is equal to X1(ri)) with respect to the base point Y(ri) in order to compute the jump conditions for the adjoint variable λ1. The residual measures the squared geodesic distance between the point Y(ri) on the fitted curve and the corresponding measurement Yi. To derive this gradient, we consider the constrained minimization problem of two points with exact matching:
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Parametric Regression on the Grassmannian
In [Paper, Algorithms 1 and 2] we need the gradient of the residuals dg(Y(ri),Yi) (here, Y(ri) is equal to X1(ri)) with respect to the base point Y(ri) in order to compute the jump conditions for the adjoint variable λ1. The residual measures the squared geodesic distance between the point Y(ri) on the fitted curve and the corresponding measurement Yi. To derive this gradient, we consider the c...
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