2008 42nd Annual Conference on Information Sciences and Systems 2008
DOI: 10.1109/ciss.2008.4558560
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Volume of geodesic balls in the real Stiefel manifold

Abstract: Volume estimates of balls in Riemannian manifolds find many applications in information theory such as in the determination of the rate-distortion tradeoff for the quantization of a source randomly distributed over such surfaces. This paper computes the precise power series expansion of volume of small geodesic balls in a real Stiefel manifold of arbitrary dimension. An application of the result obtained is also demonstrated.

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Cited by 5 publications
(9 citation statements)
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“…Similar approaches have been used in [1], [4] and [5] for the Grassmann and the real/complex Stiefel manifolds, respectively.…”
Section: Bounds On Distortion-rate Tradeoffmentioning
confidence: 95%
See 3 more Smart Citations
“…Similar approaches have been used in [1], [4] and [5] for the Grassmann and the real/complex Stiefel manifolds, respectively.…”
Section: Bounds On Distortion-rate Tradeoffmentioning
confidence: 95%
“…We extended the analysis to the more general case of the Stiefel manifold V n,k , the set of all n × k semi-orthogonal (or semi-unitary) matrices, by characterizing the distortion-rate tradeoff D * (K) of a source invariantly distributed over it -for real matrices in [4] and for complex matrices in [5]. In this paper, we consider the more general case of arbitrary covariance matrix feedback by relating it to quantization on P F (n), F = R or C, and by providing asymptotically tight bounds for D * (K) for uniform sources over them.…”
Section: Introductionmentioning
confidence: 99%
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“…In [10], a closed-form expression on the volume of a small ball in Grassmannians under the chordal distance was derived. Finally, a power series expansion of the (unnormalized) volume of small ball valid for any Riemann manifold is leveraged in [11], [12]. This provides a powerful tool-in order to obtain a normalized volume expansion, it suffices to divide by the overall volume of the manifold.…”
Section: Introductionmentioning
confidence: 99%