Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"joint sparsity"”.
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A unified framework for identifiability analysis in bilinear inverse problems
… solve BIPs. For example, subspace constraints or sparsity constraints are imposed to reduce the search space. These approaches have shown some success in practice. However, there are few results on uniqueness in BIPs. For most BIPs, the fundamental question of under what condition the problem …
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Transfer learning algorithms for image classification
… with meta-data. In the second part we present a joint sparsity transfer algorithm for image classification. Our algorithm is based on the observation that related categories might be learnable using only a small subset of shared relevant features. To find these features we propose to train …
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Bilinear inverse problems with sparsity: Optimal identifiability conditions and efficient recovery
… structures of natural signals (e.g., subspace or sparsity) are exploited. However, there are few theoretical justifications for using such structures for BIPs. We consider two types of BIPs, blind deconvolution (BD) and blind gain and phase calibration (BGPC), with subspace or sparsity structures. …
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Sparse Support Matrix Machines for the Classification of Corrupted Data
… problem of high dimensionality data by jointly optimizing the both regularizer and hinge loss. We combine the hinge loss and regularization terms as spectral elastic net penalty. The regularization term which promotes the structural sparsity and shares similar sparsity patterns across …
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Advanced imaging via multiplexed sensing and compressive sensing
… total-variation measure TVL1, which enforces the sparsity and the directional continuity in the partial gradient domain. Theoretical and experimental results show that this new TVL1 achieves higher recovery accuracy than the previous TV measure TVL1L2 in decoding images from compressive …
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Discrete and Continuous Sparse Recovery Methods and Their Applications
… for the synthesis model, we exploit the joint sparsity between the mismatch parameters and original sparse signal. We demonstrate that by exploiting this information, we can obtain a robust reconstruction under mild conditions on the sensing matrix. This model is very useful …
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Adaptive nonlocal and structured sparse signal modeling and applications
… the third part of the dissertation, we propose a joint sparse and low-rank model, dubbed STROLLR, to better represent natural images. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using …
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Efficient and guaranteed algorithms for sparse inverse problems
… propose robust and efficient algorithms for the joint sparse recovery problem in compressed sensing, which simultaneously recover the supports of jointly sparse signals from their multiple measurement vectors obtained through a common sensing matrix. In a favorable situation, the unknown matrix, …