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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Sparse Inverse Problems over Measures: Equivalence of the Conditional Gradient and Exchange Methods [PDF]
SIAM Journal on Optimization, 2019 We study an optimization program over nonnegative Borel measures that encourages sparsity in its solution.
Armin Eftekhari, Andrew Thompson
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Sparsity Equivalence of Anisotropic Decompositions
, 2011 Anisotropic decompositions using representation systems such as curvelets, contourlet, or shearlets have recently attracted significantly increased attention due to the fact that they were shown to provide optimally sparse approximations of functions exhibiting singularities on lower dimensional embedded manifolds.
Gitta Kutyniok
openaire +4 more sources
On the Equivalence Between a Minimal Codomain Cardinality Riesz Basis Construction, a System of Hadamard–Sylvester Operators, and a Class of Sparse, Binary Optimization Problems [PDF]
IEEE Transactions on Signal Processing, 2014 Piecewise, low-order polynomial, Riesz basis families are constructed such that they share the same coefficient functionals of smoother, orthonormal bases in a localized indexing subset.
James D. B. Nelson
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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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IEEE AccessThis study introduces a robust approach for denoising pressure signals by integrating Improved Complete Ensemble Empirical Mode Decomposition (ICEEMDAN), Continuous Mean Square Error (CMSE) analysis, optimal wavelet selection, and wavelet thresholding ...
Van-Trung Nguyen, Minh-Tien Nguyen
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Partial Disentanglement via Mechanism Sparsity [PDF]
arXiv.org, 2022 Disentanglement via mechanism sparsity was introduced recently as a principled approach to ex-tract latent factors without supervision when the causal graph relating them in time is sparse, and/or when actions are observed and affect them sparsely ...
Sébastien Lachapelle +1 more
semanticscholar +1 more source
Matrix Pontryagin principle approach to controllability metrics maximization under sparsity constraints [PDF]
arXiv.org, 2022 Controllability maximization problem under sparsity constraints is a node selection problem that selects inputs that are effective for control in order to minimize the energy to control for desired state.
Tomofumi Ohtsuka +2 more
semanticscholar +1 more source
Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work [PDF]
Aperture Neuro, 2021 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
semanticscholar +1 more source
Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model. [PDF]
Sci Rep, 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 ...
Kim H, Lee ER, Park S.
europepmc +2 more sources