Results 31 to 40 of about 26,519,848 (254)
Principal component analysis [PDF]
, 2014 Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis.
Bro, Rasmus +4 more
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Ensemble Principal Component Analysis
IEEE AccessEfficient representations of data are essential for processing, exploration, and human understanding, and Principal Component Analysis (PCA) is one of the most common dimensionality reduction techniques used for the analysis of large, multivariate ...
Olga Dorabiala +2 more
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Principal Component Analysis In Radar Polarimetry [PDF]
Advances in Radio Science, 2005 Second order moments of multivariate (often Gaussian) joint probability density functions can be described by the covariance or normalised correlation matrices or by the Kennaugh matrix (Kronecker matrix).
A. Danklmayer, M. Chandra, E. Lüneburg
doaj
Craniofacial similarity analysis through sparse principal component analysis.
PLoS ONE, 2017 The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery.
Junli Zhao +7 more
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GrIP-PCA: Grassmann Iterative P-Norm Principal Component Analysis
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.
Breton Minnehan +2 more
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Facial feature extraction and principal component analysis for face detection in color images [PDF]
, 2004 A hybrid technique based on facial feature extraction and Principal Component Analysis (PCA) is presented for frontal face detection in color images. Facial features such as eyes and mouth are automatically detected based on properties of the associated ...
O'Connor, Noel E. +8 more
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Automatic Image Alignment Using Principal Component Analysis
IEEE Access, 2018 We present an automatic technique for image alignment using a principal component analysis (PCA) that broadly consists of two steps. The first step is the segmentation of the region of interest by thresholding.
Hafiz Zia Ur Rehman, Sungon Lee
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The importance of selecting the optimal number of principal components for fault detection using principal component analysis [PDF]
, 2012 Includes summary.Includes bibliographical references.Fault detection and isolation are the two fundamental building blocks of process monitoring. Accurate and efficient process monitoring increases plant availability and utilization.
Khwambala, Patricia Helen
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Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities
Revista Mexicana de Economía y Finanzas Nueva Época REMEF, 2021 The objective of this paper is to compare four dimension reduction techniques used for extracting the underlying systematic risk factors driving returns on equities of the Mexican Market.
Rogelio Ladrón de Guevara Cortés +2 more
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Wind forecasting using Principal Component Analysis [PDF]
, 2014 We present a new statistical wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict the wind speed using an ensemble of dynamically similar past events.
Fruh, Wolf-Gerrit; id_orcid +3 more
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