Results 41 to 50 of about 2,237,659 (305)
Experimental validation of the reverse polar decomposition of depolarizing Mueller matrices [PDF]
Journal of the European Optical Society-Rapid Publications, 2007 We experimentally assess the validity of the reverse polar decomposition (R. Ossikovski et al., Opt. Lett. 32, 689 (2007)), which describes any Mueller matrix as a product of a depolarizer, a diattenuator and a retarder with the diattenuator placed after
Anastasiadou Makrina +4 more
doaj +1 more source
Molecular Oncology, EarlyView.Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur +11 more
wiley +1 more source
Column-coherent matrix decomposition
Data Mining and Knowledge Discovery, 2023 AbstractMatrix decomposition is a widely used tool in machine learning with many applications such as dimension reduction or visualization. In this paper we consider decomposing X, a matrix of size $$n \times m$$ n × m , to a product WS ...
openaire +2 more sources
Elevated Connectivity During Language Processing Is Associated With Cognitive Performance in SeLECTS
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Self‐Limited Epilepsy with Centrotemporal Spikes (SeLECTS) is associated with language impairments despite seizures originating in the motor cortex, suggesting aberrant cross‐network interactions. Here we tested whether functional connectivity in SeLECTS during language tasks predicts language performance.
Wendy Qi +8 more
wiley +1 more source
A Generalized CUR Decomposition for Matrix Pairs
SIAM Journal on Mathematics of Data Science, 2022 We propose a generalized CUR (GCUR) decomposition for matrix pairs $(A, B)$. Given matrices $A$ and $B$ with the same number of columns, such a decomposition provides low-rank approximations of both matrices simultaneously, in terms of some of their rows and columns.
Perfect Y. Gidisu +1 more
openaire +6 more sources
Matrix Decomposition and Applications [PDF]
, 2023 In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the factorization of a matrix into the product of lower and upper triangular ...
Lu, Jun
core +1 more source
Fast Circulant Tensor Power Method for High-Order Principal Component Analysis
IEEE Access, 2021 To understand high-order intrinsic key patterns in high-dimensional data, tensor decomposition is a more versatile tool for data analysis than standard flat-view matrix models. Several existing tensor models aim to achieve rapid computation of high-order
Taehyeon Kim, Yoonsik Choe
doaj +1 more source
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
Learnable Graph-Regularization for Matrix Decomposition
ACM Transactions on Knowledge Discovery from Data, 2023 Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields, including computer vision, text mining, bioinformatics, and many others. They allow for embedding high-dimensional data into low-dimensional spaces, which mitigates the effects of noise and uncovers latent relations.
Penglong Zhai, Shihua Zhang
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