3,431 research outputs found
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
We study a probabilistic numerical method for the solution of both boundary
and initial value problems that returns a joint Gaussian process posterior over
the solution. Such methods have concrete value in the statistics on Riemannian
manifolds, where non-analytic ordinary differential equations are involved in
virtually all computations. The probabilistic formulation permits marginalising
the uncertainty of the numerical solution such that statistics are less
sensitive to inaccuracies. This leads to new Riemannian algorithms for mean
value computations and principal geodesic analysis. Marginalisation also means
results can be less precise than point estimates, enabling a noticeable
speed-up over the state of the art. Our approach is an argument for a wider
point that uncertainty caused by numerical calculations should be tracked
throughout the pipeline of machine learning algorithms.Comment: 11 page (9 page conference paper, plus supplements
Manifold interpolation and model reduction
One approach to parametric and adaptive model reduction is via the
interpolation of orthogonal bases, subspaces or positive definite system
matrices. In all these cases, the sampled inputs stem from matrix sets that
feature a geometric structure and thus form so-called matrix manifolds. This
work will be featured as a chapter in the upcoming Handbook on Model Order
Reduction (P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W.H.A.
Schilders, L.M. Silveira, eds, to appear on DE GRUYTER) and reviews the
numerical treatment of the most important matrix manifolds that arise in the
context of model reduction. Moreover, the principal approaches to data
interpolation and Taylor-like extrapolation on matrix manifolds are outlined
and complemented by algorithms in pseudo-code.Comment: 37 pages, 4 figures, featured chapter of upcoming "Handbook on Model
Order Reduction
Principal Boundary on Riemannian Manifolds
We consider the classification problem and focus on nonlinear methods for
classification on manifolds. For multivariate datasets lying on an embedded
nonlinear Riemannian manifold within the higher-dimensional ambient space, we
aim to acquire a classification boundary for the classes with labels, using the
intrinsic metric on the manifolds. Motivated by finding an optimal boundary
between the two classes, we invent a novel approach -- the principal boundary.
From the perspective of classification, the principal boundary is defined as an
optimal curve that moves in between the principal flows traced out from two
classes of data, and at any point on the boundary, it maximizes the margin
between the two classes. We estimate the boundary in quality with its
direction, supervised by the two principal flows. We show that the principal
boundary yields the usual decision boundary found by the support vector machine
in the sense that locally, the two boundaries coincide. Some optimality and
convergence properties of the random principal boundary and its population
counterpart are also shown. We illustrate how to find, use and interpret the
principal boundary with an application in real data.Comment: 31 pages,10 figure
The Square Root Velocity Framework for Curves in a Homogeneous Space
In this paper we study the shape space of curves with values in a homogeneous
space , where is a Lie group and is a compact Lie subgroup. We
generalize the square root velocity framework to obtain a reparametrization
invariant metric on the space of curves in . By identifying curves in
with their horizontal lifts in , geodesics then can be computed. We can also
mod out by reparametrizations and by rigid motions of . In each of these
quotient spaces, we can compute Karcher means, geodesics, and perform principal
component analysis. We present numerical examples including the analysis of a
set of hurricane paths.Comment: To appear in 3rd International Workshop on Diff-CVML Workshop, CVPR
201
Isometric action of SL(2,R) on homogeneous spaces
We investigate the SL(2,R) invariant geodesic curves with the as- sociated
invariant distance function in parabolic geometry. Parabolic geom- etry
naturally occurs in the study of SL(2,R) and is placed in between the elliptic
and the hyperbolic (also known as the Lobachevsky half-plane and 2- dimensional
Minkowski half-plane space-time) geometries. Initially we attempt to use
standard methods of finding geodesics but they lead to degeneracy in this
setup. Instead, by studying closely the two related elliptic and hyperbolic
geometries we discover a unified approach to a more exotic and less obvious
parabolic case. With aid of common invariants we describe the possible dis-
tance functions that turn out to have some unexpected, interesting properties.Comment: LaTeX, 10 pages, 9 EPS figure
Covariant un-reduction for curve matching
The process of un-reduction, a sort of reversal of reduction by the Lie group
symmetries of a variational problem, is explored in the setting of field
theories. This process is applied to the problem of curve matching in the
plane, when the curves depend on more than one independent variable. This
situation occurs in a variety of instances such as matching of surfaces or
comparison of evolution between species. A discussion of the appropriate
Lagrangian involved in the variational principle is given, as well as some
initial numerical investigations.Comment: Conference paper for MFCA201
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