Results 21 to 30 of about 184,536 (160)
Sensors, 2014 Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA.
Sai Xu +4 more
doaj +1 more source
Frontiers in Bioengineering and Biotechnology, 2021 BackgroundKinematic analysis facilitates interpreting the extent and mechanisms of motor restoration after stroke. This study was aimed to explore the kinematic components of finger-to-nose test obtained from principal component analysis (PCA) and the ...
Ze-Jian Chen +13 more
doaj +1 more source
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
PCA of PCA: principal component analysis of partial covering absorption in NGC 1365 [PDF]
Monthly Notices of the Royal Astronomical Society, 2014 We analyse 400 ks of XMM-Newton data on the active galactic nucleus NGC 1365 using principal component analysis (PCA) to identify model independent spectral components. We find two significant components and demonstrate that they are qualitatively different from those found in MCG?6-30-15 using the same method.
Parker, M. L. +3 more
openaire +4 more sources
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
Combination of DEA and PCA for Full Ranking of Decision Making Units [PDF]
مدیریت صنعتی, 2009 This paper presents a combination of Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) to reduce the dimensionality of data set. DEA is known as effective tool for assessment and benchmarking.
Mojtaba Khazaei, Hamid Reza Izadbakhsh
doaj
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