Principal Component Analysis (PCA)
Top PDF Principal Component Analysis (PCA):
Robust Principal Component Analysis Based On Trimming Around Affine Subspaces
... robust principal component analysis is to perform dimension reduction, and to find an optimal subspace of a certain dimension, then trimmed PCA is a natural ...the principal component ...
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Performance analysis and anomaly detection in wind turbines based on neural networks and principal component analysis
... performance analysis and anomaly detection (PAAD) which can detect anomalies and point out the origin of the detected ...applies principal component analysis (PCA) technique to uncover the ...
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Principal component analysis for automatic tag suggestion / Enrique Estellés Arolas, Fernando González Ladrón de Guevara and Antonio Falcó Montesinos
... and Principal Component Analysis (PCA) in order to extract all those terms that are more relevant in a web site marked in a social bookmarking ...
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Class-Conditional Probabilistic Principal Component Analysis: application to gender recognition
... The best holistic classification results are based on using Support Vector Machine (SVM) as classifiers [Moghaddam and Yang, 2002; Andreu and Mollineda, 2008; Mäkinen and Raisamo, 2008]. Moghaddam and Yang [Moghaddam and ...
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Burst Detection in Water Networks Using Principal Component Analysis
... called Principal Component Analysis (PCA), allows for a sensitive and quick analysis of the inflows into a DMA without hassling mathematical ...
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Data Reduction and Regression Using Principal Component Analysis in Qualitative Spatial Reasoning and Health Informatics
... of principal component analysis (PCA) is to reduce the dimensionality of a dataset consisting of a large number of interrelated variables, while retaining as much as possible of the variation present ...
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Potential species distribution modeling and the use of principal component analysis as predictor variables
... Through principal component analysis (PCA) the 2 sets of original variables were transformed into 4 different sets to produce the species distribution models with the modeling application in ...
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Forescat modeling of spatio-temporal raster data using principal component analysis and a neural networks - wavelet decomposition model
... a principal component analysis, then 2) individual forecasts on the most significant components or eigenvectors are calculated using a neural networks - wavelet decomposition ...
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Facial Expression Recognition using kernel principal component analysis (KPCA)
... kernel principal component analysis using the Carnegie Mellon University database as a testing ...with principal component analysis and discussed where the percentages of sucess ...
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Estimation of the underlying structure of systematic risk with the use of principal component analysis and factor analysis
... of Principal Component Analysis and Factor ...the Principal Component Analysis and the Maximum Likelihood Factor Analysis ...
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Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit to stand movement
... This paper presents an application of Functional Principal Component Analysis (FPCA) to describe inter-subject variability of multiple waveforms. This technique was applied to the study of ...
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Regionalization and classification of bioclimatic zones in the central-northeastern region of México using principal component analysis (PCA)
... We use a combination of nearly 30 years of monthly means of temperature and precipitation data from 173 meteorological stations to carry out a climate classification of the state of San Luis Potosí, México. PCA ...
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Principal component analysis applied to continuation power flow
... The simulation considers a single bus – load equivalent and controlled variation of the equivalent system impedance behind the load. Variations of data structure are proposed and analyzed in order to interpret results ...
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A Data driven Study of RR Lyrae Near IR Light Curves: Principal Component Analysis, Robust Fits, and Metallicity Estimates
... second component can make the bump at the end of the rising branch ( around phase ...third component is important to reproduce the double-peaked light curves displayed by some of the ...
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Classification of Food Spices by Proximate Content: Principal Component, Cluster, Meta-Analyses
... Proximate composition of six food spices commonly used in South-East Nigeria are classified by principal component analysis (PCAs) of constituents and spices cluster analysis (CAs). Samples ...
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Aplicació de l’espectroscòpia d’infraroig a l’anàlisi de control a les indústries adobera i paperera
... by Principal Component Analysis (PCA) and Canonical Variate Analysis (CVA) allowed the identification of the finishing treatment used on the studied leather ...
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Fault Detection and Diagnosis in a Heat Exchanger Using Dynamic Principal Component Analysis and Diagnostic Observers-Edición Única
... 5.9 Diagnostic result for emulated faults in the actuators (cases [1 2]) 50 5.10 Diagnostic result for emulated faults in the actuators (cases [3 4]) 51 5.11 Fault detection result[r] ...
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Molecular Categorization of Yams by Principal Component and Cluster Analyses
... The main aim of the present report is to develop code learning potentialities and, since molecules are more naturally described via varying size structured representation, the study of general approaches to ...
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Authentication of retail cheeses based on fatty acid composition and multivariate data analysis
... by principal component analysis (PCA) and consequent orthogonal partial least squares discrimination analysis (OPLS-DA) using fatty acid (FA) profile differ- ences is ...Multivariate ...
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Wine science in the metabolomics era
... approaches used in wine metabolomics studies are principal component analysis PCA.. is based on dimension reduction and is often used as a preprocessing step prior to the.[r] ...
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