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Component analysis

Principal component analysis for automatic tag suggestion / Enrique Estellés Arolas, Fernando González Ladrón de Guevara and Antonio Falcó Montesinos

Principal component analysis for automatic tag suggestion / Enrique Estellés Arolas, Fernando González Ladrón de Guevara and Antonio Falcó Montesinos

... applications are based on URLs that store (the web resources they mark) and the tags they use to describe them. Regarding the latter, recent studies show that more than 50% of the tags used to mark a URL can be ...

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Class-Conditional Probabilistic Principal Component Analysis: application to gender recognition

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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Performance analysis and anomaly detection in wind turbines based on neural networks and principal component analysis

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 ...principal component analysis (PCA) technique to uncover the root of the ...

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Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit to stand movement

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 sit-to-stand ...

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Data Reduction and Regression Using Principal Component Analysis in Qualitative Spatial Reasoning and Health Informatics

Data Reduction and Regression Using Principal Component Analysis in Qualitative Spatial Reasoning and Health Informatics

... 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 in the ...

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Use of Independent Component Analysis in the extraction of Artifacts of the Mismatch Negativity Response

Use of Independent Component Analysis in the extraction of Artifacts of the Mismatch Negativity Response

... the analysis of this signal can be di- fficult by a low SNR due to artifacts present when the signal is ...Independent Component Analysis (ICA) to preprocess and eliminate these ...

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Robust Principal Component Analysis Based On Trimming Around Affine Subspaces

Robust Principal Component Analysis Based On Trimming Around Affine Subspaces

... Principal Component Analysis (PCA) is a technique for reducing dimen- sionality in multivariate data analysis. For p-dimensional observations, and a given dimension d, with d typically much lower ...

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Estimation of the underlying structure of systematic risk with the use of principal component analysis and factor analysis

Estimation of the underlying structure of systematic risk with the use of principal component analysis and factor analysis

... We present an improved methodology to estimate the underlying structure of systematic risk in the Mexican Stock Exchange with the use of Principal Component Analysis and Factor Analysis. We consider ...

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Feature selection in pathological voice classification using dinamyc of component analysis

Feature selection in pathological voice classification using dinamyc of component analysis

... The aim of this paper is the comparison and evaluation of dynamic feature sets that are suitable for classification of pathological voices using HMM. For this purpose, the feature selection methodology presented in [10], ...

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Burst Detection in Water Networks Using Principal Component Analysis

Burst Detection in Water Networks Using Principal Component Analysis

... Abstract: The following work presents a multivariate statistical technique applied to the control of water inflows into district metering areas (DMA) of urban networks. This technique, called Principal Component ...

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Potential species distribution modeling and the use of principal component analysis as predictor variables

Potential species distribution modeling and the use of principal component analysis as predictor variables

... 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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Facial Expression Recognition using kernel principal component analysis (KPCA)

Facial Expression Recognition using kernel principal component analysis (KPCA)

... principal component analysis using the Carnegie Mellon University database as a testing ...principal component analysis and discussed where the percentages of sucess found with the database is ...

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Principal component analysis applied to continuation power flow

Principal component analysis applied to continuation power flow

... RINCIPAL Component Analysis (PCA) is a powerful tool for data processing which includes data reduction and feature extraction. In the context of power systems, CPA presents very interesting perspectives for ...

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Principal and independent component analysis applied to noise reduction in electrocardiographic signals

Principal and independent component analysis applied to noise reduction in electrocardiographic signals

... Principal component analysis and Principal component analysis, they will be applied on a data base of electrocardiogram who have been taken on humans ...

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Artifact elimination from EEG signals using parametric modeling restoration and independent component analysis

Artifact elimination from EEG signals using parametric modeling restoration and independent component analysis

... The second approach involves the use of ICA when there are more available elec- trodes, as occurs in a complete 10-20 arrangement. ICA has been widely used in EEG for artifact removal, but since the EEG signals do not ...

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Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

... We validated ourmethod by compar- ing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data set[r] ...

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Latest developments in data analysis tools for disruption prediction and  for the exploration of multimachine operational spaces.

Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces.

... data analysis tools to both predict the occurrence of disruptions and to investigate the operational spaces of devices, with the long term goal of advancing the understanding of the physics of these events and to ...

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Hybrid Higher-Order Statistics  Learning in Multiuser Detection

Hybrid Higher-Order Statistics Learning in Multiuser Detection

... principal component analysis in image or financial data processing, in monitoring, etc In addition, they have been lately applied to other problems such as spread-spectrum-based communications [6], [7], ...

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Authentication of retail cheeses based on fatty acid composition and multivariate data analysis

Authentication of retail cheeses based on fatty acid composition and multivariate data analysis

... principal component analysis (PCA) and consequent orthogonal partial least squares discrimination analysis (OPLS-DA) using fatty acid (FA) profile differ- ences is ...Multivariate analysis ...

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... principal component analysis suggest that first component has an endogenous meaning (stomata length and width have higher weights than the two other variables) whereas the sec- ond component ...

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