Results 71 to 80 of about 4,208,641 (395)
Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders [PDF]
Physical Review E, 2017 We examine unsupervised machine learning techniques to learn features that best describe configurations of the two-dimensional Ising model and the three-dimensional XY model.
S. Wetzel
semanticscholar +1 more source
FEBS Letters, EarlyView.In vivo IL‐10 produced by tissue‐resident tolDC is involved in maintaining/inducing tolerance. Depending on the agent used for ex vivo tolDC generation, cells acquire common features but prime T cells towards anergy, FOXP3+ Tregs, or Tr1 cells according to the levels of IL‐10 produced. Ex vivo‐induced tolDC were administered to patients to re‐establish/
Konstantina Morali +3 more
wiley +1 more source
Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis
Remote Sensing, 2020 Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent ...
Alina L. Machidon +4 more
doaj +1 more source
Manifold Regularized Principal Component Analysis Method Using L2,p-Norm
Mathematics, 2022 The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples.
Minghua Wan +3 more
doaj +1 more source
On the number of principal components in high dimensions [PDF]
Biometrika 105 (2018) 389-402, 2017 We consider the problem of how many components to retain in the application of principal component analysis when the dimension is much higher than the number of observations. To estimate the number of components, we propose to sequentially test skewness of the squared lengths of residual scores that are obtained by removing leading principal components.
arxiv +1 more source
Projected principal component analysis in factor models [PDF]
, 2016 This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employs principal component analysis to the projected (smoothed) data matrix onto a given linear space spanned by covariates. When it applies to high-dimensional factor
Fan, Jianqing, Liao, Yuan, Wang, Weichen
core +1 more source
FEBS Letters, EarlyView.Metabolic dysfunction‐associated steatotic liver disease (MASLD) affects nearly one‐third of the global population and poses a significant risk of progression to cirrhosis or liver cancer. Here, we discuss the roles of hepatic dendritic cell subtypes in MASLD, highlighting their distinct contributions to disease initiation and progression, and their ...
Camilla Klaimi +3 more
wiley +1 more source
Insights into PI3K/AKT signaling in B cell development and chronic lymphocytic leukemia
FEBS Letters, EarlyView.This Review explores how the phosphoinositide 3‐kinase and protein kinase B pathway shapes B cell development and drives chronic lymphocytic leukemia, a common blood cancer. It examines how signaling levels affect disease progression, addresses treatment challenges, and introduces novel experimental strategies to improve therapies and patient outcomes.
Maike Buchner
wiley +1 more source
A principal component analysis for trees
The Annals of Applied Statistics, 2009 The active field of Functional Data Analysis (about understanding the variation in a set of curves) has been recently extended to Object Oriented Data Analysis, which considers populations of more general objects. A particularly challenging extension of this set of ideas is to populations of tree-structured objects.
Aydın, Burcu +4 more
openaire +4 more sources
Van Trees inequality, group equivariance, and estimation of principal subspaces [PDF]
arXiv, 2021 We establish non-asymptotic lower bounds for the estimation of principal subspaces. As applications, we obtain new results for the excess risk of principal component analysis and the matrix denoising problem.
arxiv