Nonparametric Sparsity and Regularization by Rosasco, Lorenzo Andrea, Villa, Silvia, Mosci, Sofia, Santoro, Matteo, Verri, Alessandro

“…Based on this intuition we propose a new notion of nonparametric sparsity and a corresponding least squares regularization scheme. …”
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Rank-Sparsity Incoherence for Matrix Decomposition by Chandrasekaran, Venkat, Sanghavi, Sujay, Parrilo, Pablo A., Willsky, Alan S.

“…We develop a notion of rank-sparsity incoherence, expressed as an uncertainty principle between the sparsity pattern of a matrix and its row and column spaces, and we use it to characterize both fundamental identifiability as well as (deterministic) sufficient conditions for exact recovery. …”
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Necessary and Sufficient Conditions for Sparsity Pattern Recovery by Fletcher, Alyson K., Goyal, Vivek K., Rangan, Sundeep

“…he paper considers the problem of detecting the sparsity pattern of a k -sparse vector in BBR n from m random noisy measurements. …”
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Better approximations for Tree Sparsity in Nearly-Linear Time by Backurs, Arturs, Indyk, Piotr, Schmidt, Ludwig

“…The Tree Sparsity problem is defined as follows: given a node-weighted tree of size n and an integer k, output a rooted subtree of size k with maximum weight. …”
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Transform-domain sparsity regularization for inverse problems in geosciences by Jarapour, Behnam, Goyal, Vivek K., McLaughlin, Dennis, Freeman, William T.

“…Where we have tested our sparsity regulariza-tion approach, it has performed better than traditional alter-natives.…”
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Notes on PCA, Regularization, Sparsity and Support Vector Machines by Poggio, Tomaso, Girosi, Federico

“…In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). …”
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A Sparsity Detection Framework for On–Off Random Access Channels by Fletcher, Alyson K., Rangan, Sundeep, Goyal, Vivek K.

“…Using recent sparsity results, we derive upper and lower bounds on the capacities of these channels. …”
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Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction by Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, Goyal, Vivek K.

“…To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. …”
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Sparsity Maximization under a Quadratic Constraint with Applications in Filter Design by Wei, Dennis, Oppenheim, Alan V.

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Regularizing GRAPPA using simultaneous sparsity to recover de-noised images by Goyal, Vivek K., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, Weller, Daniel Stuart

“…This novel combination of GRAPPA and CS regularizes the GRAPPA kernel computation step using a simultaneous sparsity penalty function of the coil images. This approach can be implemented by formulating the problem as the joint optimization of the least squares fit of the kernel to the ACS lines and the sparsity of the images generated using GRAPPA with the kernel.…”
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Exploiting sparsity in time-of-flight range acquisition using a single time-resolved sensor by Kirmani, Ahmed, Colaco, Andrea B., Wong, Franco N. C., Goyal, Vivek K.

“…Then, a convex optimization that exploits sparsity of the Laplacian of the depth map of a typical scene determines correspondences between spatial positions and depths. …”
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Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing by Cetin, Mujdat, Stojanovic, Ivana, Onhon, Ozben, Varshney, Kush, Samadi, Sadegh, Karl, William Clem, Willsky, Alan S.

“…This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. …”
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Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain by Belloni, Alexandre, Chen, Daniel, Chernozhukov, Victor, Hansen, Christian

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Nearly Linear-Time Model-Based Compressive Sensing by Hegde, Chinmay, Indyk, Piotr, Schmidt, Ludwig

“…In particular, two main barriers arise: (i) Existing recovery algorithms involve several projections into the structured sparsity model. For several sparsity models (such as tree-sparsity), the best known model-projection algorithms run in time Ω(kn), which can be too slow for large k. …”
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Delay-Aware Wide-Area Control of Power Systems over Sparse Communications with Analytical Guarantees by Chakrabortty, Aranya, Hussain, Alefiya, Dibaji, SeyedMehran, Annaswamy, Anuradha M

“…Both sparsity features are introduced in the control design using the well-known Geromel algorithm. …”
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Least Squares After Model Selection in High-dimensional Sparse Models by Belloni, Alexandre, Chernozhukov, Victor V.

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Simple Type-Level Unsupervised POS Tagging by Lee, Yoong Keok, Haghighi, Aria, Barzilay, Regina

“…Part-of-speech (POS) tag distributions are known to exhibit sparsity — a word is likely to take a single predominant tag in a corpus. …”
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Lasso Methods for Gaussian Instrumental Variables Models by Belloni, Alexandre, Chernozhukov, Victor, Hansen, Christian

“…We derive asymptotic distributions for the resulting IV estimators and provide conditions under which these sparsity-based IV estimators are asymptotically oracle-efficient. …”
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Simultaneously Sparse Solutions to Linear Inverse Problems with Multiple System Matrices and a Single Observation Vector by Adalsteinsson, Elfar, Zelinski, Adam C., Goyal, Vivek K.

“…Experiments involve sparsity pattern recovery in noiseless and noisy settings and MRI RF pulse design.…”
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Sequential Compressed Sensing by Malioutov, Dmitry M., Sanghavi, Sujay R., Willsky, Alan S.

“…Existing results in compressed sensing literature have focused on characterizing the achievable performance by bounding the number of samples required for a given level of signal sparsity. However, using these bounds to minimize the number of samples requires a priori knowledge of the sparsity of the unknown signal, or the decay structure for near-sparse signals. …”
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