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74
RIP-Based Near-Oracle Performance Guarantees for SP, CoSaMP, and IHT
"... Abstract—This correspondence presents an average case denoising performance analysis for SP, CoSaMP, and IHT algorithms. This analysis considers the recovery of a noisy signal, with the assumptions that it is corrupted by an additive random zero-mean white Gaussian noise and has a-sparse representat ..."
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Cited by 20 (4 self)
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-sparse representation with respect to a known dictionary D. The proposed analysis is based on the RIP, establishing a near-oracle performance guarantee for each of these algorithms. Beyond bounds for the reconstruction error that hold with high probability, in this work we also provide a bound for the average error
Coherence-based near-oracle performance guarantees for sparse estimation under Gaussian noise
- in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP 2010
, 2010
"... We consider the problem of estimating a deterministic sparse vector x0 from underdetermined measurements Ax0 + w, where w represents white Gaussian noise and A is a given deterministic dictionary. We analyze the performance of three sparse estimation algorithms: basis pursuit denoising, orthogonal m ..."
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Cited by 3 (2 self)
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matching pursuit, and thresholding. These approaches are shown to achieve near-oracle performance with high probability, assuming that x0 is sufficiently sparse. Our results are non-asymptotic and are based only on the coherence of A, so that they are applicable to arbitrary dictionaries. Index Terms
1 Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques from Noisy Measurements
"... ar ..."
Near-oracle performance of greedy block-sparse estimation techniques from noisy measurements
- Signal Process., 2010 [Online]. Available: http://arxiv. org/pdf/1009.0906
"... Abstract—This paper examines the ability of greedy algorithms to estimate a block sparse parameter vector from noisy measurements. In particular, block sparse versions of the orthogonal matching pursuit and thresholding algorithms are analyzed under both adversarial and Gaussian noise models. In the ..."
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Cited by 18 (2 self)
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with the actual performance of block and non-block algorithms, identifying situations in which block sparse techniques improve upon the scalar sparsity approach. Specifically, we show that block sparse methods are particularly successful when the atoms within each block are nearly orthogonal.
The adaptive LASSO and its oracle properties
- Journal of the American Statistical Association
"... The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain sc ..."
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Cited by 683 (10 self)
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scenarios where the lasso is inconsistent for variable selection. We then propose a new version of the lasso, called the adaptive lasso, where adaptive weights are used for penalizing different coefficients in the!1 penalty. We show that the adaptive lasso enjoys the oracle properties; namely, it performs
Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise
"... We consider the problem of estimating a deterministic sparse vector x0 from underdetermined measurements Ax0 + w, where w represents white Gaussian noise and A is a given deterministic dictionary. We analyze the performance of three sparse estimation algorithms: basis pursuit denoising (BPDN), orth ..."
Abstract
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Cited by 43 (15 self)
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), orthogonal matching pursuit (OMP), and thresholding. These algorithms are shown to achieve near-oracle performance with high probability, assuming that x0 is sufficiently sparse. Our results are non-asymptotic and are based only on the coherence of A, so that they are applicable to arbitrary dictionaries
Advance Access publication on ?
, 2011
"... SUMMARY We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors are significant. The method is a modification of Lasso, called square-root Lasso. The method nei ..."
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neither relies on the knowledge of the standard deviation σ of the regression errors nor does it need to pre-estimate σ. Despite not knowing σ, square-root Lasso achieves near-oracle performance, attaining the prediction norm convergence rate σ (s/n) log p, and thus matching the performance of the Lasso
An analysis of database workload performance on simultaneous multithreaded processors
- In Proceedings of the 25th Annual International Symposium on Computer Architecture
, 1998
"... Simultaneous multithreading (SMT) is an architectural technique in which the processor issues multiple instructions from multiple threads each cycle. While SMT has been shown to be effective on scientific workloads, its performance on database systems is still an open question. In particular, databa ..."
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Cited by 134 (14 self)
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, database systems have poor cache performance, and the addition of multithreading has the potential to exacerbate cache conflicts. This paper examines database performance on SMT processors using traces of the Oracle database management system. Our research makes three contributions. First, it characterizes
Results 1 - 10
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