Results 11 to 20 of about 20,309 (231)
Qualitative Methods for the Inverse Obstacle Problem: A Comparison on Experimental Data
Journal of Imaging, 2019 Qualitative methods are widely used for the solution of inverse obstacle problems. They allow one to retrieve the morphological properties of the unknown targets from the scattered field by avoiding dealing with the problem in its full non-linearity and ...
Martina T. Bevacqua, Roberta Palmeri
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A Multichannel Spatial Compressed Sensing Approach for Direction of Arrival Estimation [PDF]
, 2010 The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-642-15995-4_57ESPRC Leadership Fellowship EP/G007144/1EPSRC Platform Grant EP/045235/1EU FET-Open Project FP7-ICT-225913 ...
D. Malioutov +7 more
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An Interpretable and Scalable Recommendation Method Based on Network Embedding
IEEE Access, 2019 Matrix factorization is a widely used technique in recommender systems. However, its performance is often affected by the sparsity and the scalability. To address the above-mentioned problem, we propose an interpretable and scalable recommendation method
Xuejian Zhang +4 more
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Block-Sparse Recovery via Convex Optimization [PDF]
, 2012 Given a dictionary that consists of multiple blocks and a signal that lives in the range space of only a few blocks, we study the problem of finding a block-sparse representation of the signal, i.e., a representation that uses the minimum number of ...
Ehsan Elhamifar +3 more
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Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization [PDF]
, 2011 Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics.
Giannakis, Georgios B., Mateos, Gonzalo
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PLoS ONE, 2018 We proposed a new efficient image denoising scheme, which mainly leads to four important contributions whose approaches are different from existing ones.
Shuting Cai +5 more
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Statistical inference in compound functional models [PDF]
, 2012 We consider a general nonparametric regression model called the compound model. It includes, as special cases, sparse additive regression and nonparametric (or linear) regression with many covariates but possibly a small number of relevant covariates ...
Dalalyan, Arnak +2 more
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Design of robust constant beamwidth beamformer with maximal sparsity
Tongxin xuebao, 2015 To reduce the complexity of broadband array systems,an optimization model was built based on the analysis of the sparsity of the broadband array.The objective function was the convex combination of sensor and TDL sparsity with the constraint of constant ...
Kai WU, Tao SU, Qiang LI, Xue-hui HE
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Sparse Inverse Covariance Estimation for Chordal Structures
, 2017 In this paper, we consider the Graphical Lasso (GL), a popular optimization problem for learning the sparse representations of high-dimensional datasets, which is well-known to be computationally expensive for large-scale problems.
agrawal +7 more
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Smoothing gradient descent algorithm for the composite sparse optimization
AIMS MathematicsComposite sparsity generalizes the standard sparsity that considers the sparsity on a linear transformation of the variables. In this paper, we study the composite sparse optimization problem consisting of minimizing the sum of a nondifferentiable loss ...
Wei Yang, Lili Pan, Jinhui Wan
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