Results 1 to 10 of about 20,469 (205)
$NP/CMP$ Equivalence: A Phenomenon Hidden Among Sparsity Models $l_{0}$ Minimization and $l_{p}$ Minimization for Information Processing [PDF]
IEEE Transactions on Information Theory, 2015 In this paper, we have proved that in every underdetermined linear system $Ax=b$ , there corresponds a constant $p^{*}(A,b)>0$ such that every solution to the $l_{p}$ -norm minimization problem also solves the $l_{0}$ -norm minimization problem whenever $0 . This phenomenon is named $NP/CMP$ equivalence.
Shigang Yue, Haiyang Li
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Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states. [PDF]
PLoS Computational BiologyGraphical models are widely used to represent dependence structures in biological systems, where directed edges may encode causal relationships under appropriate assumptions. We present baycn (BAYesian Causal Network), a novel approximate Bayesian method
Evan A Martin +2 more
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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, Weisheng Li, Xiaobo Luo
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SHAPE RECONSTRUCTION VIA EQUIVALENCE PRINCIPLES,CONSTRAINED INVERSE SOURCE PROBLEMS AND SPARSITY PROMOTION [PDF]
Progress in Electromagnetics Research, 2017 A new approach for position and shape reconstruction of both penetrable and impenetrable objects from the measurements of the scattered fields is introduced and described. The approach takes advantage of the fact that for perfect electric conductors the induced currents are localized on the boundary, and equivalent sources also placed on the surface of
Martina T Bevacqua, Tommaso Isernia
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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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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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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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