Results 191 to 200 of about 7,174 (250)
Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization. [PDF]
Hozumi Y, Wei GW.
europepmc +1 more source
Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. [PDF]
Saxena S +10 more
europepmc +1 more source
ABSTRACT Hydraulic manipulators exhibit strong coupling, pronounced nonlinearities, and significant modeling uncertainties, which hinder high‐precision motion control. This paper proposes a finite‐time disturbance observer–based nonlinear robust adaptive control (RAC‐FTDO) framework enhanced by a physically consistent dynamic parameter identification ...
Tianyu Gao +3 more
wiley +1 more source
Cauchy hyper-graph Laplacian nonnegative matrix factorization for single-cell RNA-sequencing data analysis. [PDF]
Wang GF, Shen L.
europepmc +1 more source
We propose a novel deep learning algorithm for predicting the myelin water fraction from multiple gradient‐echo or spin‐echo pulse sequences arising in magnetic resonance relaxometry (MRR) measurements of the human brain. Our method incorporates both regularized nonlinear least squares and pure deep learning through a concatenation paradigm known as ...
Mirage Modi +7 more
wiley +1 more source
Extended nonnegative matrix factorization for dynamic functional connectivity analysis of fMRI data. [PDF]
Long Z, Xu Y, Zou W, Duan Y, Yao L.
europepmc +1 more source
A Preconditioned Majorization‐Minimization Method for ℓ2$$ {\ell}^2 $$‐ℓq$$ {\ell}^q $$ Minimization
ABSTRACT The need to minimize a linear combination of an expression that involves an ℓq$$ {\ell}^q $$‐norm of a linear transformation of the computed solution and the ℓ2$$ {\ell}^2 $$‐norm of the residual error arises in image restoration as well as in statistics.
A. Buccini +3 more
wiley +1 more source
Nonnegative matrix factorization for the identification of pressure ulcer risks from seating interface pressures in people with spinal cord injury. [PDF]
Yang TD, Jan YK.
europepmc +1 more source
Row‐Aware Randomized SVD With Applications
ABSTRACT The randomized singular value decomposition proposed in [28] has certainly become one of the most well‐established randomization‐based algorithms in numerical linear algebra. The key ingredient of the entire procedure is the computation of a subspace which is close to the column space of the target matrix A∈ℝm×n$$ \mathbf{A}\in {\mathbb{R}}^{m\
Davide Palitta, Sascha Portaro
wiley +1 more source

