Abstract:We consider random sampling in finitely generated shift-invariant spaces V (Φ) ⊂ L 2 (R n ) generated by a vector Φ = (ϕ 1 , . . . , ϕ r ) ∈ L 2 (R n ) r . Following the approach introduced by Bass and Gröchenig, we consider certain relatively compact subsets V R,δ (Φ) of such a space, defined in terms of a concentration inequality with respect to a cube with side lengths R. Under very mild assumptions on the generators, we show that for R sufficiently large, taking O(R n log(R n 2 /α ′ )) many random samples … Show more
“…Randomized algorithms in a context of continuous frames were presented in the past. Relevant sampling is a line of work in which integral transforms are randomly discretized [5,24,42,49]. While the goal in our approach is to approximate the continuous frame with a quadrature sum, the goal in relevant sampling is to sample discrete frames from continuous frames.…”
Section: Randomized Quadrature Approximations Of Continuous Framesmentioning
confidence: 99%
“…Definition24 Let H be a Hilbert space that we call the signal space. A class of signals R ⊂ H is a (possibly non-linear) subset of H. A sequence of discretizations of R is a sequence of (generally non-linear) subspaces {V M ⊂ H} ∞ M=1 that satisfies the following condition: for every s ∈ R there is a sequence {s…”
This paper focuses on signal processing tasks in which the signal is transformed from the signal space to a higher dimensional coefficient space (also called phase space) using a continuous frame, processed in the coefficient space, and synthesized to an output signal. We show how to approximate such methods, termed phase space signal processing methods, using a Monte Carlo method. As opposed to standard discretizations of continuous frames, based on sampling discrete frames from the continuous system, the proposed Monte Carlo method is directly a quadrature approximation of the continuous frame. We show that the Monte Carlo method allows working with highly redundant continuous frames, since the number of samples required for a certain accuracy is proportional to the dimension of the signal space, and not to the dimension of the phase space. Moreover, even though the continuous frame is highly redundant, the Monte Carlo samples are spread uniformly, and hence represent the coefficient space more faithfully than standard frame discretizations.
“…Randomized algorithms in a context of continuous frames were presented in the past. Relevant sampling is a line of work in which integral transforms are randomly discretized [5,24,42,49]. While the goal in our approach is to approximate the continuous frame with a quadrature sum, the goal in relevant sampling is to sample discrete frames from continuous frames.…”
Section: Randomized Quadrature Approximations Of Continuous Framesmentioning
confidence: 99%
“…Definition24 Let H be a Hilbert space that we call the signal space. A class of signals R ⊂ H is a (possibly non-linear) subset of H. A sequence of discretizations of R is a sequence of (generally non-linear) subspaces {V M ⊂ H} ∞ M=1 that satisfies the following condition: for every s ∈ R there is a sequence {s…”
This paper focuses on signal processing tasks in which the signal is transformed from the signal space to a higher dimensional coefficient space (also called phase space) using a continuous frame, processed in the coefficient space, and synthesized to an output signal. We show how to approximate such methods, termed phase space signal processing methods, using a Monte Carlo method. As opposed to standard discretizations of continuous frames, based on sampling discrete frames from the continuous system, the proposed Monte Carlo method is directly a quadrature approximation of the continuous frame. We show that the Monte Carlo method allows working with highly redundant continuous frames, since the number of samples required for a certain accuracy is proportional to the dimension of the signal space, and not to the dimension of the phase space. Moreover, even though the continuous frame is highly redundant, the Monte Carlo samples are spread uniformly, and hence represent the coefficient space more faithfully than standard frame discretizations.
“…They obtained the probabilistic sampling inequality for band-limited functions on R in [3] and the same for band-limited functions on R d in [4]. Random sampling in shift-invariant spaces was studied in [26,24,9]. Yang and Tao in [25] studied random sampling and gave an approximation model for signals having bounded derivatives.…”
In this paper, the problem of reconstruction of signals in mixed Lebesgue spaces from their random average samples has been studied. Probabilistic sampling inequalities for certain subsets of shiftinvariant spaces have been derived. It is shown that the probabilities increase to one when the sample size increases. Further, explicit reconstruction formulae for signals in these subsets have been obtained for which the numerical simulations have also been performed.
“…In recent years, random sampling has become a rather active area of research, due to its simplicity, flexibility and effectiveness, researchers investigate random sampling for different function spaces [1,2,6,7,18]. Besides, centered discrepancy of random sampling and Latin hypercube sampling are investigated in [19].…”
In this paper, we study bounds of expected L 2 −discrepancy to give mean square error of uniform integration approximation for functions in Sobolev space H 1 (K), where H is a reproducing Hilbert space with kernel K. Better order O(N −1− 1 d ) of approximation error is obtained, comparing with previously known rate O(N −1 ) using crude Monte Carlo method. Secondly, we use expected L p −discrepancy bound(p ≥ 1) of stratified samples to give several upper bounds of p-moment of integral approximation error in general Sobolev space F * d,q .
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