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Record W2331944129 · doi:10.1109/taes.2015.150456

Recovery probability analysis for sparse signals via OMP

2015· article· en· W2331944129 on OpenAlexaff
Mingbo Niu, Soheil Salari, Il‐Min Kim, François Chan, Sreeraman Rajan

Bibliographic record

VenueIEEE Transactions on Aerospace and Electronic Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicSparse and Compressive Sensing Techniques
Canadian institutionsCarleton UniversityRoyal Military College of CanadaQueen's UniversityOkanagan College
Fundersnot available
KeywordsMatching pursuitMutual coherenceCoherence (philosophical gambling strategy)Computer scienceGaussianMatrix (chemical analysis)AlgorithmCompressed sensingProbability distributionPattern recognition (psychology)Sparse matrixProbability density functionCovariance matrixBasis pursuitSignal processingArtificial intelligenceMathematicsStatisticsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

It is known that use of a random measurement (sensing) matrix usually results in good recovery performance via orthogonal matching pursuit. This paper provides the probability of ensuring the recovery of sparse signals using orthogonal matching pursuit for the case where all entries of the measurement matrix are independently selected from a Gaussian distribution. The analysis relies on the mutual-coherence property of the sensing matrix.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.235
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2015
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Aerospace and Electronic SystemsSame topicSparse and Compressive Sensing TechniquesFrench-language works237,207