Results 61 to 70 of about 6,413,007 (222)
The Singular Value Expansion for Arbitrary Bounded Linear Operators
Mathematics, 2020 The singular value decomposition (SVD) is a basic tool for analyzing matrices. Regarding a general matrix as defining a linear operator and choosing appropriate orthonormal bases for the domain and co-domain allows the operator to be represented as ...
Daniel K. Crane, Mark S. Gockenbach
doaj +1 more source
Dynamic survival risk prediction with time‐varying high‐dimensional images
Canadian Journal of Statistics, EarlyView.Abstract Integrating longitudinal data with survival models is a prevalent strategy for dynamic survival risk prediction while accounting for subjects' longitudinally observed variables. However, existing methods primarily focus on scalar longitudinal data and seldom tackle the complexities associated with high‐dimensional longitudinal imaging data ...
Bingfan Liu +7 more
wiley +1 more source
An Aggregative High-Order Singular Value Decomposition Method in Edge Computing
IEEE Access, 2020 In edge computing, for dimensionality reduction and core data extraction, both edge computing node (ECN) and cloud server may implement a high-order singular value decomposition (HOSVD) algorithm before data are passed to local computing models. However,
Junhua Chen +3 more
doaj +1 more source
Optimal subsampling for regression with mixed‐type predictors
Canadian Journal of Statistics, EarlyView.Abstract Subsampling has emerged as an appealing strategy to mitigate the computational and storage challenges imposed by large datasets. Recent subsampling techniques have shown notable computational gains for data dominated by numerical predictors. However, real‐world datasets frequently contain both numerical and categorical predictors.
Jiaqing Zhu, Lin Wang, Fasheng Sun
wiley +1 more source
Unraveling complexity: Singular value decomposition in complex experimental data analysis
SciPost Physics CoreAnalyzing complex experimental data with multiple parameters is challenging. We propose using Singular Value Decomposition (SVD) as an effective solution.
Judith F. Stein, Aviad Frydman, Richard Berkovits
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Transformation of Non-Euclidean Space to Euclidean Space for Efficient Learning of Singular Vectors
IEEE Access, 2020 Singular value decomposition (SVD) is a popular technique to extract essential information by reducing the dimension of a feature set. SVD is able to analyze a vast matrix in spite of a relatively low computational cost.
Seunghyun Lee, Byung Cheol Song
doaj +1 more source
Journal of Intelligent Medicine, EarlyView.SubDIVIDE integrates a physics‐consistent DIVIDE subspace directly into MRI reconstruction. By embedding a low‐rank basis derived from a large DIVIDE signal dictionary into a joint ADMM optimization—together with composite sensitivity maps that absorb inter‐b‐value phase errors—the framework recovers quantitative microstructural maps from highly ...
Cheng Yang +4 more
wiley +1 more source
Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery
Shock and Vibration, 2009 Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is ...
Junsheng Cheng +3 more
doaj +1 more source
Adaptive template-updating strategy based on singular value decomposition
, 2007 The problem of image matching and target tracking based on singular value decomposition (SVD) is discussed. The SVD has robust performance that is invariant to image disturbance and it makes the singular value credible to represent the image as an ...
Shi ZL(史泽林) +2 more
core
SVDmodel/SVD: GYE regeneration failure
, 2021 Final version of SVD used for the regeneration failure study in GYE published at Global Change Biology. This version includes the SVD fire module, the matrix module.
SVD
core +1 more source