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Sparsity and Infinite Divisibility [PDF]
IEEE Transactions on Information Theory, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Amini, Arash, Unser, Michael
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Applied Sciences, 2023 Single-cell RNA sequencing (scRNA-seq) has become a powerful technique to investigate cellular heterogeneity and complexity in various fields by revealing the gene expression status of individual cells. Despite the undeniable benefits of scRNA-seq, it is
Tiantian Liu, Yuanyuan Li
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Journal of Marine Science and Engineering, 2023 Digital beamforming techniques find wide applications in the field of underwater acoustic array signal processing. However, their azimuthal resolution has long been constrained by the Rayleigh limit, consequently limiting their detection performance.
Fan Yin +6 more
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Regularizers for structured sparsity [PDF]
Advances in Computational Mathematics, 2011 We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. This problem is relevant in machine learning, statistics and signal processing. It is well known that a linear regression can benefit from knowledge that the underlying regression vector is sparse.
Charles A. Micchelli +2 more
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Comparing measures of sparsity [PDF]
2008 IEEE Workshop on Machine Learning for Signal Processing, 2008 Sparsity of representations of signals has been shown to be a key concept of fundamental importance in fields such as blind source separation, compression, sampling and signal analysis. The aim of this paper is to compare several commonlyused sparsity measures based on intuitive attributes.
Niall P. Hurley, Scott T. Rickard
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CoRR, 2013 Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013) introduced a novel regularization method for compressive imaging in the context of compressed sensing with coherent redundant dictionaries. The approach relies on the observation that natural images exhibit strong average sparsity over multiple coherent frames.
Carrillo Rafael +2 more
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Antenna Array Calibration Using a Sparse Scene
IEEE Open Journal of Antennas and Propagation, 2021 In radar systems, antenna arrays acquire direction-dependent information to localize targets or create images of the environment. However, because of unknown complex amplitudes per channel and mutual coupling, a calibration is necessary for good ...
Johanna Geiss +3 more
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De-Biased Graphical Lasso for High-Frequency Data
Entropy, 2020 This paper develops a new statistical inference theory for the precision matrix of high-frequency data in a high-dimensional setting. The focus is not only on point estimation but also on interval estimation and hypothesis testing for entries of the ...
Yuta Koike
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Sparse Support Tensor Machine with Scaled Kernel Functions
Mathematics, 2023 As one of the supervised tensor learning methods, the support tensor machine (STM) for tensorial data classification is receiving increasing attention in machine learning and related applications, including remote sensing imaging, video processing, fault
Shuangyue Wang, Ziyan Luo
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Learning with structured sparsity
Proceedings of the 26th Annual International Conference on Machine Learning, 2009 This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea that has become popular in recent years.
Junzhou Huang +2 more
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