Results 91 to 100 of about 196,468 (204)
Model Selection for Non-Negative Tensor Factorization with Minimum Description Length
Entropy, 2019 Non-negative tensor factorization (NTF) is a widely used multi-way analysis approach that factorizes a high-order non-negative data tensor into several non-negative factor matrices.
Yunhui Fu +2 more
doaj +1 more source
Is the World Getting Smaller? Network‐Driven Heterogeneity in Gravity‐Model Elasticities
The World Economy, EarlyView.ABSTRACT Gravity models constitute the workhorse of empirical international trade research, yet the temporal stability of their core parameters—distance and GDP elasticities—remains underexplored, particularly in relation to the evolving structure of trade networks.
Zsolt T. Kosztyán +2 more
wiley +1 more source
Cross-Work Theme Identification in Long Novels via Nonnegative Tensor Factorization
MathematicsIdentifying the major themes and recurring motifs of an author’s long novels is a basic task in literary studies. To support this task in a scalable way while retaining within-novel narrative variation, we model an author corpus as a third-order ...
Yiying Chen, Maosheng Liu, Yuning Yang
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Geophysical Research Letters, Volume 53, Issue 21, 16 November 2026.Abstract Groundwater contamination assessments based on sparse wells rarely provide continuous hydraulic head and contaminant concentration fields for plume interpretation. Visually plausible reconstructions may not retain the transport information needed for physical diagnosis.
Zhiyu Jiao +9 more
wiley +1 more source
On the geometric interpretation of the nonnegative rank [PDF]
The nonnegative rank of a nonnegative matrix is the minimum number of nonnegative rank-one factors needed to reconstruct it exactly. The problem of determining this rank and computing the corresponding nonnegative factors is difficult; however it has ...
GILLIS, Nicolas, GLINEUR, François
core
Coupled Nonnegative Matrix/Tensor Factorization in Brain Imaging Data
, 2020 Continuous advancement of brain imaging techniques has witnessed data analysis methods evolving from matrix component analysis to tensor component analysis, from individual analysis to group analysis regarding the analysis of brain data with multi-set/
Wang, Xiulin +4 more
core +1 more source
The Impact of BOLD‐Induced Linewidth Modulation on Functional 1H MRS Analysis
NMR in Biomedicine, Volume 39, Issue 11, November 2026.Functional MRS studies face a technical challenge: BOLD‐related lineshape changes can be mistaken for genuine metabolic changes, causing ~1% glutamate estimation bias at 3 and 7 T across conventional (LCModel, ABfit‐reg) and 2D fitting methods. Using synthetic data, this study shows that explicit BOLD linewidth correction—via dynamic lineshape ...
Martin Wilson +2 more
wiley +1 more source
On the conformal‐biharmonic stability of the identity map of Einstein manifolds
Bulletin of the London Mathematical Society, Volume 58, Issue 11, November 2026.Abstract The identity map of an Einstein manifold is a critical point of both the classical energy functional and the conformal‐bienergy functional. In this paper, we investigate the conformal‐biharmonic stability of the identity map of compact Einstein manifolds of dimension at least four and with nonnegative scalar curvature, and we compare it with ...
Volker Branding +2 more
wiley +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral unmixing (HU), an essential procedure for various environmental applications, has garnered significant attention within remote sensing communities. Among different groups of HU methods, nonnegative matrix factorization (NMF)-based ones have
Saeid Gholinejad, Alireza Amiri-Simkooei
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Compression of hyper-spectral images using an accelerated nonnegative tensor decomposition
Open Physics, 2017 Nonnegative tensor Tucker decomposition (NTD) in a transform domain (e.g., 2D-DWT, etc) has been used in the compression of hyper-spectral images because it can remove redundancies between spectrum bands and also exploit spatial correlations of each band.
Li Jin, Liu Zilong
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