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Found 5 papers, 0 papers with code
Dual-Space Analysis of the Sparse Linear Model
In contrast, for analyses of update rules and sparsity properties of local and global solutions, as well as extensions to more general likelihood models, we can leverage coefficient-space techniques developed for Type I and apply them to Type II.
Sparse Estimation with Structured Dictionaries
In the vast majority of recent work on sparse estimation algorithms, performance has been evaluated using ideal or quasi-ideal dictionaries (e. g., random Gaussian or Fourier) characterized by unit $\ell_2$ norm, incoherent columns or features.
Sparse Estimation Using General Likelihoods and Non-Factorial Priors
Finding maximally sparse representations from overcomplete feature dictionaries frequently involves minimizing a cost function composed of a likelihood (or data fit) term and a prior (or penalty function) that favors sparsity.
Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG
In a restricted setting, the proposed method is shown to have theoretically zero bias estimating both the location and orientation of multi-component dipoles even in the presence of correlations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA.
A New View of Automatic Relevance Determination
The result is an efficient algorithm that can be implemented using standard convex programming toolboxes and is guaranteed to converge to a stationary point unlike existing methods.
