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Social-sparsity brain decoders: faster spatial sparsity [PDF]
2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016 Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions.
Kowalski, Matthieu +2 more
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, 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.
Carrillo, Rafael E. +2 more
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Sparse algorithm of robust extreme learning machine with l_0-norm
Nantong Daxue xuebao. Ziran kexue ban, 2023 Extreme learning machine(ELM) has shown great potential in machine learning because of its high learning rate and strong generalization performance.
WANG Xiaoxue; WANG Kuaini
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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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RULE-BASED APPROACH FOR CONTEXT-AWARE COLLABORATIVE RECOMMENDER SYSTEM
Jordanian Journal of Computers and Information Technology, 2022 Sparsity is a serious problem of collaborative recommendation approach that has a considerable effect on recommendation quality. Contextual information is introduced in traditional recommendation systems besides users and items information to reduce ...
Soulef Benhamdi +3 more
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Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates
Mathematics, 2022 As applied sciences grow by leaps and bounds, semiparametric regression analyses have broad applications in various fields, such as engineering, finance, medicine, and public health.
Yunquan Song, Yaqi Liu, Hang Su
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Combinatorica, 2015 We prove that posets of bounded height whose cover graphs belong to a fixed class with bounded expansion have bounded dimension. Bounded expansion, introduced by Ne et il and Ossona de Mendez as a model for sparsity in graphs, is a property that is naturally satisfied by a wide range of graph classes, from graph structure theory (graphs excluding a ...
Joret, Gwenaël +2 more
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An improved sparsity-aware normalized least-mean-square scheme for underwater communication
ETRI Journal, 2023 Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC.
Anand Kumar, Prashant Kumar
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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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