Results 31 to 40 of about 415,661 (284)
Urban water quality evaluation using multivariate analysis [PDF]
Acta Montanistica Slovaca, 2007 A data set, obtained for the sake of drinking water quality monitoring, was analysed by multivariate methods. Principal component analysis (PCA) reduced the data dimensionality from 18 original physico-chemical and microbiological parameters determined ...
Petr Praus
doaj
Multiscale principal component analysis
, 2013 Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances.
Akinduko, A. A., Gorban, A. N.
core +1 more source
Brent Crude Oil Daily Price Forecast by Combining Principal Components Analysis and Support Vector Regression methods [PDF]
Pizhūhishnāmah-i Iqtiṣād-i Inirzhī-i Īrān, 2017 Anticipating process of crude oil prices and its fluctuations volatility has always been one of the challenges the traders face in the exchange oil markets. This study estimates the Brent crude oil daily price forecast with a proposed hybrid model.
Elham Hajikaram, Roya Darabi
doaj +1 more source
Geohazard Mechanics, 2023 In order to predict the coal outburst risk quickly and accurately, a PCA-FA-SVM based coal and gas outburst risk prediction model was designed. Principal component analysis (PCA) was used to pre-process the original data samples, extract the principal ...
Chaojun Fan +3 more
doaj +1 more source
GrIP-PCA: Grassmann Iterative P-Norm Principal Component Analysis [PDF]
IEEE Open Journal of Signal Processing, 2020 Principal component analysis is one of the most commonly used methods for dimensionality reduction in signal processing. However, the most commonly used PCA formulation is based on the L2-norm, which can be highly influenced by outlier data. In recent years, there has been growing interest in the development of more robust PCA methods.
Breton Minnehan +2 more
openaire +2 more sources
Principal Flow Patterns across renewable electricity networks
, 2018 Using Principal Component Analysis (PCA), the nodal injection and line flow patterns in a network model of a future highly renewable European electricity system are investigated. It is shown that the number of principal components needed to describe 95$\%
Brown, Tom +5 more
core +1 more source
Structural insights into an engineered feruloyl esterase with improved MHET degrading properties
FEBS Letters, EarlyView.A feruloyl esterase was engineered to mimic key features of MHETase, enhancing the degradation of PET oligomers. Structural and computational analysis reveal how a point mutation stabilizes the active site and reshapes the binding cleft, expading substrate scope.
Panagiota Karampa +5 more
wiley +1 more source
Memory efficient PCA methods for large group ICA
Frontiers in Neuroscience, 2016 Principal component analysis (PCA) is widely used for data reduction in group independent component analysis (ICA) of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components ...
Srinivas eRachakonda +6 more
doaj +1 more source
Animals, 2021 Cross-resistance occurs between antimicrobials with either similar mechanisms of action and/or similar chemical structures, or even between unrelated antimicrobials.
Daniel Nenene Qekwana +2 more
doaj +1 more source
Kernel principal component analysis (KPCA) for the de-noising of communication signals [PDF]
, 2002 This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a ...
Koutsogiannis, G., Soraghan, J.J.
core +2 more sources