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首页> 外文期刊>Information Theory, IEEE Transactions on >Recipes for Stable Linear Embeddings From Hilbert Spaces to {mathbb {R}}^{m}
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Recipes for Stable Linear Embeddings From Hilbert Spaces to {mathbb {R}}^{m}

机译:从希尔伯特空间到{mathbb {R}} ^ {m}的稳定线性嵌入的食谱

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摘要

We consider the problem of constructing a linear map from a Hilbert space H (possibly infinite dimensional) to ℝm that satisfies a restricted isometry property (RIP) on an arbitrary signal model, i.e., a subset of H. We present a generic framework that handles a large class of low-dimensional subsets but also unstructured and structured linear maps. We provide a simple recipe to prove that a random linear map satisfies a general RIP with high probability. We also describe a generic technique to construct linear maps that satisfy the RIP. Finally, we detail how to use our results in several examples, which allow us to recover and extend many known compressive sampling results.
机译:我们考虑在任意信号模型(即H的一个子集)上构造从希尔伯特空间H(可能是无穷大维)到ℝm满足受限等距特性(RIP)的线性映射的问题。我们提出了一个处理一大类低维子集,但也包括非结构化和结构化的线性图。我们提供了一个简单的方法来证明随机线性映射以高概率满足一般RIP。我们还描述了一种通用技术来构造满足RIP的线性映射。最后,我们在几个示例中详细说明了如何使用我们的结果,这些示例使我们可以恢复和扩展许多已知的压缩采样结果。

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