Results 31 to 40 of about 614,012 (260)
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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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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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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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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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
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Interpretable Functional Principal Component Analysis
Biometrics, 2015 SummaryFunctional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations ...
Lin, Zhenhua +2 more
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Parametric Functional Principal Component Analysis
Biometrics, 2017 Summary Functional principal component analysis (FPCA) is a popular approach in functional data analysis to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs).
Sang, Peijun +2 more
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Stable Analysis of Compressive Principal Component Pursuit
Algorithms, 2017 Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures from a small set of linear measurements. Pervious works mainly focus on the analysis of the existence and uniqueness.
Qingshan You, Qun Wan
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Principal component analysis applied to remote sensing
Modelling in Science Education and Learning, 2013 The main objective of this article was to show an application of principal component analysis (PCA) which is used in two science degrees. Particularly, PCA analysis was used to obtain information of the land cover from satellite images.
Javier Estornell +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Chemotherapy‐induced peripheral neuropathy remains a major complication in pediatric cancer, with disrupted somatosensory and nociceptive processing being a key aspect. This review synthesizes empirical studies on alterations in somatosensory and nociceptive processing in children and adolescents with cancer.
Julia Schweiger +4 more
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