Results 11 to 20 of about 20,280 (112)
Remote Sensing, 2023 Hyperspectral image (HSI) super-resolution is a vital technique that generates high spatial-resolution HSI (HR-HSI) by integrating information from low spatial-resolution HSI with high spatial-resolution multispectral image (MSI).
Yidong Peng +3 more
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Sparsest Univariate Learning Models Under Lipschitz Constraint
IEEE Open Journal of Signal Processing, 2022 Beside the minimizationof the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability. Driven by these principles, we propose continuous-domain formulations for one-dimensional regression problems.
Shayan Aziznejad +2 more
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Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work
Aperture Neuro, 2023 Deconvolution of the hemodynamic response is an important step to access short timescales of brain activity recorded by functional magnetic resonance imaging (fMRI).
Eneko Uruñuela +3 more
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Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model
Scientific Reports, 2023 Due to the prevalence of complex data, data heterogeneity is often observed in contemporary scientific studies and various applications. Motivated by studies on cancer cell lines, we consider the analysis of heterogeneous subpopulations with binary ...
Hyunjin Kim, Eun Ryung Lee, Seyoung Park
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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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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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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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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
core +1 more source
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
core +4 more sources