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Analysis Model

The Experts below are selected from a list of 122598 Experts worldwide ranked by ideXlab platform

Mi Wang – 1st expert on this subject based on the ideXlab platform

  • An impact Analysis Model for distributed Web service proces
    The 2010 14th International Conference on Computer Supported Cooperative Work in Design, 2010
    Co-Authors: Mi Wang

    Abstract:

    In the distributed cross-organization business process, each organization only knows its private orchestration and partial public choreography, It doesn’t have any information about the global process. If one organization has to change, it is difficult to compute the impact imposed on other organizations. In this paper, we propose a method to get the global services dependency matrix automatically through analysing the orchestrations and choreography. Then we introduces the service impact Analysis Model based dependency matrix and intraservice and inter-service change propagation, through which we can ascertained the impact caused by the changes.

Wenbo Li – 2nd expert on this subject based on the ideXlab platform

  • A modified greedy Analysis pursuit algorithm for the cosparse Analysis Model
    Numerical Algorithms, 2016
    Co-Authors: Jicheng Li, Wenbo Li

    Abstract:

    In the past decade, the sparse representation synthesis Model has been deeply researched and widely applied in signal processing. Recently, a cosparse Analysis Model has been introduced as an interesting alternative to the sparse representation synthesis Model. The sparse synthesis Model pay attention to non-zero elements in a representation vector x, while the cosparse Analysis Model focuses on zero elements in the Analysis representation vector Ωx. This paper mainly considers the problem of the cosparse Analysis Model. Based on the greedy Analysis pursuit algorithm, by constructing an adaptive weighted matrix Wk?1, we propose a modified greedy Analysis pursuit algorithm for the sparse recovery problem when the signal obeys the cosparse Model. Using a weighted matrix, we fill the gap between greedy algorithm and relaxation techniques. The standard Analysis shows that our algorithm is convergent. We estimate the error bound for solving the cosparse Analysis Model, and then the presented simulations demonstrate the advantage of the proposed method for the cosparse inverse problem.

  • ICNC – The reweighed greedy Analysis pursuit algorithm for the cosparse Analysis Model
    2015 11th International Conference on Natural Computation (ICNC), 2015
    Co-Authors: Jicheng Li, Wenbo Li

    Abstract:

    Recently, a cosparse Analysis Model has been introduced as an interesting alternative to the sparse representation synthesis Model. This Model is focused on zero elements in the Analysis representation vector rather than non-zero elements. Hence, finding cosparse solutions is a problem of important significance in signal processing. In this paper, we construct an adaptive weighted matrix in the greedy Analysis pursuit algorithm and propose the reweighed greedy Analysis pursuit (ReGAP) algorithm for cosparse signal reconstruction with noise. Using a weighted matrix, we fill the gap between greedy and convex relaxation techniques. Theoretical Analysis shows that our algorithm is convergent. We estimate the error bound of ReGAP algorithm with cosparse Analysis Model, and then simulation results demonstrate that our algorithm is feasible and effective.

  • The reweighed greedy Analysis pursuit algorithm for the cosparse Analysis Model
    2015 11th International Conference on Natural Computation (ICNC), 2015
    Co-Authors: Jicheng Li, Wenbo Li

    Abstract:

    Recently, a cosparse Analysis Model has been introduced as an interesting alternative to the sparse representation synthesis Model. This Model is focused on zero elements in the Analysis representation vector rather than non-zero elements. Hence, finding cosparse solutions is a problem of important significance in signal processing. In this paper, we construct an adaptive weighted matrix in the greedy Analysis pursuit algorithm and propose the reweighed greedy Analysis pursuit (ReGAP) algorithm for cosparse signal reconstruction with noise. Using a weighted matrix, we fill the gap between greedy and convex relaxation techniques. Theoretical Analysis shows that our algorithm is convergent. We estimate the error bound of ReGAP algorithm with cosparse Analysis Model, and then simulation results demonstrate that our algorithm is feasible and effective.

Mike E Davies – 3rd expert on this subject based on the ideXlab platform

  • Greedy-Like Algorithms for the Cosparse Analysis Model
    Linear Algebra and its Applications, 2014
    Co-Authors: Raja Giryes, Michael Elad, Remi Gribonval, Mike E Davies

    Abstract:

    The cosparse Analysis Model has been introduced recently as an interesting alternative to the standard sparse synthesis approach. A prominent question brought up by this new construction is the Analysis pursuit problem – the need to find a signal belonging to this Model, given a set of corrupted measurements of it. Several pursuit methods have already been proposed based on l1 relaxation and a greedy approach. In this work we pursue this question further, and propose a new family of pursuit algorithms for the cosparse Analysis Model, mimicking the greedy-like methods – compressive sampling matching pursuit (CoSaMP), subspace pursuit (SP), iterative hard thresholding (IHT) and hard thresholding pursuit (HTP). Assuming the availability of a near optimal projection scheme that finds the nearest cosparse subspace to any vector, we provide performance guarantees for these algorithms. Our theoretical study relies on a restricted isometry property adapted to the context of the cosparse Analysis Model. We explore empirically the performance of these algorithms by adopting a plain thresholding projection, demonstrating their good performance.

  • the cosparse Analysis Model and algorithms
    Applied and Computational Harmonic Analysis, 2013
    Co-Authors: Mike E Davies, Michael Elad, Remi Gribonval

    Abstract:

    Abstract After a decade of extensive study of the sparse representation synthesis Model, we can safely say that this is a mature and stable field, with clear theoretical foundations, and appealing applications. Alongside this approach, there is an Analysis counterpart Model, which, despite its similarity to the synthesis alternative, is markedly different. Surprisingly, the Analysis Model did not get a similar attention, and its understanding today is shallow and partial. In this paper we take a closer look at the Analysis approach, better define it as a generative Model for signals, and contrast it with the synthesis one. This work proposes effective pursuit methods that aim to solve inverse problems regularized with the AnalysisModel prior, accompanied by a preliminary theoretical study of their performance. We demonstrate the effectiveness of the Analysis Model in several experiments, and provide a detailed study of the Model associated with the 2D finite difference Analysis operator, a close cousin of the TV norm.