arXiv subjects

Yulong Liu

Publications and source records attributed to Yulong Liu.

65 records · Page 4Linked to original sources

arXiv2018

Localization from Incomplete Euclidean Distance Matrix: Performance Analysis for the SVD-MDS Approach

Localizing a cloud of points from noisy measurements of a subset of pairwise distances has applications in various areas, such as sensor network localization and reconstruction of protein conformations from NMR measurements. In [1], Drineas et al. proposed a natural two-stage approach, named SVD-MDS, for this purpose. This approach consists of a low-rank matrix completion algorithm, named SVD-Reconstruct, to estimate random missing distances, and the classic multidimensional scaling (MDS) method to estimate the positions of nodes. In this paper, we present a detailed analysis for this method. More specifically, we first establish error bounds for Euclidean distance matrix (EDM) completion in both expectation and tail forms. Utilizing these results, we then derive the error bound for the recovered positions of nodes. In order to assess the performance of SVD-Reconstruct, we present the minimax lower bound of the zero-diagonal, symmetric, low-rank matrix completion problem by Fano's method. This result reveals that when the noise level is low, the SVD-Reconstruct approach for Euclidean distance matrix completion is suboptimal in the minimax sense; when the noise level is high, SVD-Reconstruct can achieve the optimal rate up to a constant factor.

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arXiv2018

Phase Transition of Convex Programs for Linear Inverse Problems with Multiple Prior Constraints

A sharp phase transition emerges in convex programs when solving the linear inverse problem, which aims to recover a structured signal from its linear measurements. This paper studies this phenomenon in theory under Gaussian random measurements. Different from previous studies, in this paper, we consider convex programs with multiple prior constraints. These programs are encountered in many cases, for example, when the signal is sparse and its $\ell_2$ norm is known beforehand, or when the signal is sparse and non-negative simultaneously. Given such a convex program, to analyze its phase transition, we introduce a new set and a new cone, called the prior restricted set and prior restricted cone, respectively. Our results reveal that the phase transition of a convex problem occurs at the statistical dimension of its prior restricted cone. Moreover, to apply our theoretical results in practice, we present two recipes to accurately estimate the statistical dimension of the prior restricted cone. These two recipes work under different conditions, and we give a detailed analysis for them. To further illustrate our results, we apply our theoretical results and the estimation recipes to study the phase transition of two specific problems, and obtain computable formulas for the statistical dimension and related error bounds. Simulations are provided to demonstrate our results.

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arXiv2017

Recovery of Structured Signals with Prior Information via Maximizing Correlation

This paper considers the problem of recovering a structured signal from a relatively small number of noisy measurements with the aid of a similar signal which is known beforehand. We propose a new approach to integrate prior information into the standard recovery procedure by maximizing the correlation between the prior knowledge and the desired signal. We then establish performance guarantees (in terms of the number of measurements) for the proposed method under sub-Gaussian measurements. Specific structured signals including sparse vectors, block-sparse vectors, and low-rank matrices are also analyzed. Furthermore, we present an interesting geometrical interpretation for the proposed procedure. Our results demonstrate that if prior information is good enough, then the proposed approach can (remarkably) outperform the standard recovery procedure. Simulations are provided to verify our results.

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arXiv2017

Stable Recovery of Structured Signals From Corrupted Sub-Gaussian Measurements

This paper studies the problem of accurately recovering a structured signal from a small number of corrupted sub-Gaussian measurements. We consider three different procedures to reconstruct signal and corruption when different kinds of prior knowledge are available. In each case, we provide conditions (in terms of the number of measurements) for stable signal recovery from structured corruption with added unstructured noise. Our results theoretically demonstrate how to choose the regularization parameters in both partially and fully penalized recovery procedures and shed some light on the relationships among the three procedures. The key ingredient in our analysis is an extended matrix deviation inequality for isotropic sub-Gaussian matrices, which implies a tight lower bound for the restricted singular value of the extended sensing matrix. Numerical experiments are presented to verify our theoretical results.

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arXiv2017

Corrupted Sensing with Sub-Gaussian Measurements

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arXiv2017

On the Phase Transition of Corrupted Sensing

In \cite{FOY2014}, a sharp phase transition has been numerically observed when a constrained convex procedure is used to solve the corrupted sensing problem. In this paper, we present a theoretical analysis for this phenomenon. Specifically, we establish the threshold below which this convex procedure fails to recover signal and corruption with high probability. Together with the work in \cite{FOY2014}, we prove that a sharp phase transition occurs around the sum of the squares of spherical Gaussian widths of two tangent cones. Numerical experiments are provided to demonstrate the correctness and sharpness of our results.

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arXiv2017

Compressed Sensing with Prior Information via Maximizing Correlation

Compressed sensing (CS) with prior information concerns the problem of reconstructing a sparse signal with the aid of a similar signal which is known beforehand. We consider a new approach to integrate the prior information into CS via maximizing the correlation between the prior knowledge and the desired signal. We then present a geometric analysis for the proposed method under sub-Gaussian measurements. Our results reveal that if the prior information is good enough, then the proposed approach can improve the performance of the standard CS. Simulations are provided to verify our results.

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arXiv2015

Spot size measurement of flash-radiography source utilizing the pinhole imaging method

The spot size of the x-ray source is a key parameter of a flash-radiography facility, which is usually quoted as an evaluation of the resolving power. The pinhole imaging technique is applied to measure the spot size of the Dragon-I linear induction accelerator, by which a two-dimensional spatial distribution of the source spot is obtained. Experimental measurements are performed to measure the spot image when the transportation and focusing of the electron beam is tuned by adjusting the currents of solenoids in the downstream section. The spot size of full-width at half maximum and that defined from the spatial frequency at half peak value of the modulation transfer function are calculated and discussed.

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arXiv2012

Fast thresholding algorithms with feedbacks for sparse signal recovery

We provide another framework of iterative algorithms based on thresholding, feedback and null space tuning for sparse signal recovery arising in sparse representations and compressed sensing. Several thresholding algorithms with various feedbacks are derived, which are seen as exceedingly effective and fast. Convergence results are also provided. The core algorithm is shown to converge in finite many steps under a (preconditioned) restricted isometry condition. The algorithms are seen as particularly effective for large scale problems. Numerical studies about the effectiveness and the speed of the algorithms are also presented.

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arXiv2012

Compressed Sensing with General Frames via Optimal-dual-based $\ell_1$-analysis

Compressed sensing with sparse frame representations is seen to have much greater range of practical applications than that with orthonormal bases. In such settings, one approach to recover the signal is known as $\ell_1$-analysis. We expand in this article the performance analysis of this approach by providing a weaker recovery condition than existing results in the literature. Our analysis is also broadly based on general frames and alternative dual frames (as analysis operators). As one application to such a general-dual-based approach and performance analysis, an optimal-dual-based technique is proposed to demonstrate the effectiveness of using alternative dual frames as analysis operators. An iterative algorithm is outlined for solving the optimal-dual-based $\ell_1$-analysis problem. The effectiveness of the proposed method and algorithm is demonstrated through several experiments.

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arXiv2012

Performance Analysis of $\ell_1$-synthesis with Coherent Frames

Signals with sparse frame representations comprise a much more realistic model of nature than that with orthonomal bases. Studies about the signal recovery associated with such sparsity models have been one of major focuses in compressed sensing. In such settings, one important and widely used signal recovery approach is known as $\ell_1$-synthesis (or Basis Pursuit). We present in this article a more effective performance analysis (than what are available) of this approach in which the dictionary $\Dbf$ may be highly, and even perfectly correlated. Under suitable conditions on the sensing matrix $\Phibf$, an error bound of the recovered signal $\hat{\fbf}$ (by the $\ell_1$-synthesis method) is established. Such an error bound is governed by the decaying property of $\tilde{\Dbf}_{\text{o}}^*\fbf$, where $\fbf$ is the true signal and $\tilde{\Dbf}_{\text{o}}$ denotes the optimal dual frame of $\Dbf$ in the sense that $\|\tilde{\Dbf}_{\text{o}}^*\hat{\fbf}\|_1$ produces the smallest $\|\tilde{\Dbf}^*\tilde{\fbf}\|_1$ in value among all dual frames $\tilde{\Dbf}$ of $\Dbf$ and all feasible signals $\tilde{\fbf}$. This new performance analysis departs from the usual description of the combo $\Phibf\Dbf$, and places the description on $\Phibf$. Examples are demonstrated to show that when the usual analysis fails to explain the working performance of the synthesis approach, the newly established results do.

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