160 research outputs found
Sphere packing bounds in the Grassmann and Stiefel manifolds
Applying the Riemann geometric machinery of volume estimates in terms of
curvature, bounds for the minimal distance of packings/codes in the Grassmann
and Stiefel manifolds will be derived and analyzed. In the context of
space-time block codes this leads to a monotonically increasing minimal
distance lower bound as a function of the block length. This advocates large
block lengths for the code design.Comment: Replaced with final version, 11 page
Density of Spherically-Embedded Stiefel and Grassmann Codes
The density of a code is the fraction of the coding space covered by packing
balls centered around the codewords. This paper investigates the density of
codes in the complex Stiefel and Grassmann manifolds equipped with the chordal
distance. The choice of distance enables the treatment of the manifolds as
subspaces of Euclidean hyperspheres. In this geometry, the densest packings are
not necessarily equivalent to maximum-minimum-distance codes. Computing a
code's density follows from computing: i) the normalized volume of a metric
ball and ii) the kissing radius, the radius of the largest balls one can pack
around the codewords without overlapping. First, the normalized volume of a
metric ball is evaluated by asymptotic approximations. The volume of a small
ball can be well-approximated by the volume of a locally-equivalent tangential
ball. In order to properly normalize this approximation, the precise volumes of
the manifolds induced by their spherical embedding are computed. For larger
balls, a hyperspherical cap approximation is used, which is justified by a
volume comparison theorem showing that the normalized volume of a ball in the
Stiefel or Grassmann manifold is asymptotically equal to the normalized volume
of a ball in its embedding sphere as the dimension grows to infinity. Then,
bounds on the kissing radius are derived alongside corresponding bounds on the
density. Unlike spherical codes or codes in flat spaces, the kissing radius of
Grassmann or Stiefel codes cannot be exactly determined from its minimum
distance. It is nonetheless possible to derive bounds on density as functions
of the minimum distance. Stiefel and Grassmann codes have larger density than
their image spherical codes when dimensions tend to infinity. Finally, the
bounds on density lead to refinements of the standard Hamming bounds for
Stiefel and Grassmann codes.Comment: Two-column version (24 pages, 6 figures, 4 tables). To appear in IEEE
Transactions on Information Theor
Metric Entropy of Homogeneous Spaces
For a (compact) subset of a metric space and , the {\em
covering number} is defined as the smallest number of
balls of radius whose union covers . Knowledge of the {\em
metric entropy}, i.e., the asymptotic behaviour of covering numbers for
(families of) metric spaces is important in many areas of mathematics
(geometry, functional analysis, probability, coding theory, to name a few). In
this paper we give asymptotically correct estimates for covering numbers for a
large class of homogeneous spaces of unitary (or orthogonal) groups with
respect to some natural metrics, most notably the one induced by the operator
norm. This generalizes earlier author's results concerning covering numbers of
Grassmann manifolds; the generalization is motivated by applications to
noncommutative probability and operator algebras. In the process we give a
characterization of geodesics in (or ) for a class of
non-Riemannian metric structures
Adaptation of K-means-type algorithms to the Grassmann manifold, An
2019 Spring.Includes bibliographical references.The Grassmann manifold provides a robust framework for analysis of high-dimensional data through the use of subspaces. Treating data as subspaces allows for separability between data classes that is not otherwise achieved in Euclidean space, particularly with the use of the smallest principal angle pseudometric. Clustering algorithms focus on identifying similarities within data and highlighting the underlying structure. To exploit the properties of the Grassmannian for unsupervised data analysis, two variations of the popular K-means algorithm are adapted to perform clustering directly on the manifold. We provide the theoretical foundations needed for computations on the Grassmann manifold and detailed derivations of the key equations. Both algorithms are then thoroughly tested on toy data and two benchmark data sets from machine learning: the MNIST handwritten digit database and the AVIRIS Indian Pines hyperspectral data. Performance of algorithms is tested on manifolds of varying dimension. Unsupervised classification results on the benchmark data are compared to those currently found in the literature
Linearization of Hyperbolic Finite-Time Processes
We adapt the notion of processes to introduce an abstract framework for
dynamics in finite time, i.e.\ on compact time sets. For linear finite-time
processes a notion of hyperbolicity namely exponential monotonicity dichotomy
(EMD) is introduced, thereby generalizing and unifying several existing
approaches. We present a spectral theory for linear processes in a coherent
way, based only on a logarithmic difference quotient, prove robustness of EMD
with respect to a suitable (semi-)metric and provide exact perturbation bounds.
Furthermore, we give a complete description of the local geometry around
hyperbolic trajectories, including a direct and intrinsic proof of finite-time
analogues of the local (un)stable manifold theorem and theorem of linearized
asymptotic stability. As an application, we discuss our results for ordinary
differential equations on a compact time-interval.Comment: 32 page
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