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Compressed matched filter for non-Gaussian noise

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Jakob Vovnoboy, Ami Wiesel

Applications · Submitted 3 Nov 2013

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We demonstrate the advantages of the proposed framework using synthetic simulations.

how it is stated · asserted 1.00 read the record →

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arXiv ID1311.0510 (version 1)
Pith IDE3ZVI7EL, Pith's permanent record of this paper and its review
CitationAmi Wiesel, Jakob Vovnoboy. "Compressed matched filter for non-Gaussian noise." arXiv:1311.0510 (2013). https://pith.science/paper/E3ZVI7EL
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,389 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.23 · human participants 0.02 · controlled comparison 0.08 · randomized assignment 0.03 · states quantitative result 0.05 · compares with prior results 0.32 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We consider estimation of a deterministic unknown parameter vector in a linear model with non-Gaussian noise. In the Gaussian case, dimensionality reduction via a linear matched filter provides a simple low dimensional sufficient statistic which can be easily communicated and/or stored for future inference. Such a statistic is usually unknown in the general non-Gaussian case. Instead, we propose a hybrid matched filter coupled with a randomized compressed sensing procedure, which together create a low dimensional statistic. We also derive a complementary algorithm for robust reconstruction given this statistic. Our recovery method is based on the fast iterative shrinkage and thresholding algorithm which is used for outlier rejection given the compressed data. We demonstrate the advantages of the proposed framework using synthetic simulations.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We consider estimation of a deterministic unknown parameter vector in a linear model with non-Gaussian noise. background 0.99 0.35 0.03 asserted 0.71
s1 In the Gaussian case, dimensionality reduction via a linear matched filter provides a simple low dimensional sufficient statistic which can be easily communicated and/or stored for future inference. background 1.00 0.07 0.04 asserted 0.99
s2 Such a statistic is usually unknown in the general non-Gaussian case. gap 0.98 0.07 0.03 asserted 0.97
s3 Instead, we propose a hybrid matched filter coupled with a randomized compressed sensing procedure, which together create a low dimensional statistic. contribution 0.60 0.93 0.38 asserted 0.98
s4 We also derive a complementary algorithm for robust reconstruction given this statistic. contribution 0.65 0.95 0.11 asserted 1.00
s5 Our recovery method is based on the fast iterative shrinkage and thresholding algorithm which is used for outlier rejection given the compressed data. method 0.99 0.66 0.09 asserted 1.00
s6 We demonstrate the advantages of the proposed framework using synthetic simulations. result 0.51 0.95 0.16 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

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  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We consider estimation of a deterministic unknown parameter vector in a linear model with non-Gaussian noise. In the Gaussian case, dimensionality reduction via a linear matched filter provides a simple low dimensional sufficient statistic which can be easily communicated and/or stored for future inference. Such a statistic is usually unknown in the general non-Gaussian case. Instead, we propose a hybrid matched filter coupled with a randomized compressed sensing procedure, which together create a low dimensional statistic. We also derive a complementary algorithm for robust reconstruction given this statistic. Our recovery method is based on the fast iterative shrinkage and thresholding algorithm which is used for outlier rejection given the compressed data. We demonstrate the advantages of the proposed framework using synthetic simulations.

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