Results 61 to 70 of about 2,294,043 (337)
On the explanatory power of principal components [PDF]
, 2014 We show that if we have an orthogonal base ($u_1,\ldots,u_p$) in a $p$-dimensional vector space, and select $p+1$ vectors $v_1,\ldots, v_p$ and $w$ such that the vectors traverse the origin, then the probability of $w$ being to closer to all the vectors ...
Dazard, Jean-Eudes +2 more
core
The role of fibroblast growth factors in cell and cancer metabolism
FEBS Letters, EarlyView.Fibroblast growth factor (FGF) signaling regulates crucial signaling cascades that promote cell proliferation, survival, and metabolism. Therefore, FGFs and their receptors are often dysregulated in human diseases, including cancer, to sustain proliferation and rewire metabolism.
Jessica Price, Chiara Francavilla
wiley +1 more source
Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation
EURASIP Journal on Advances in Signal Processing, 2004 Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following ...
Deniz Erdogmus +4 more
doaj +1 more source
Principal components of thermal regimes in mountain river networks [PDF]
Hydrology and Earth System Sciences, 2018 Description of thermal regimes in flowing waters is key to understanding physical processes, enhancing predictive abilities, and improving bioassessments.
D. J. Isaak +4 more
doaj +1 more source
Application of Principal Component Analysis for Steel Material Components
Kurdistan Journal of Applied Research, 2022 In this research, we made use of the principal component analysis (PCA) technique, which is a multivariate statistical method that transforms a fixed number of correlated variables into a fixed number of orthogonal, uncorrelated axes known as principal ...
Miran Othman Tofiq +1 more
doaj +1 more source
Recursive principal components analysis [PDF]
Neural Networks, 2005 A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by
openaire +3 more sources
CMB Constraints on Principal Components of the Inflaton Potential
, 2010 We place functional constraints on the shape of the inflaton potential from the cosmic microwave background through a variant of the generalized slow roll approximation that allows large amplitude, rapidly changing deviations from scale-free conditions ...
Cora Dvorkin, S. M. Leach, Wayne Hu
core +1 more source
FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source
Genetic variation among diverse safflower genotypes for some agro-morphological traits
Oilseeds and fats, crops and lipidsThis study sought to investigate the genetic diversity among 100 safflower genotypes concerning seed yield performance and seventeen morphological traits and yield components.
Sabaghnia Naser +2 more
doaj +1 more source
Probabilistic Principal Component Analysis [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1999 Summary Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood estimation of parameters in a latent variable model that is closely ...
Tipping, Michael E. +1 more
openaire +1 more source