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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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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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Uncertainty-Aware Principal Component Analysis
, 2019 We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions.
Deussen, Oliver +4 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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Local functional principal component analysis
, 2007 Covariance operators of random functions are crucial tools to study the way random elements concentrate over their support. The principal component analysis of a random function X is well-known from a theoretical viewpoint and extensively used in ...
Mas, André
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Modelling of Earphone Design Using Principal Component Analysis
Applied Sciences, 2023 This research investigated a mathematical model of earphone design with principal component analysis. Along with simplifying the design problem, a predictive model for the sound quality indicators, namely, total harmonic distortion, power of output ...
Lucas Kwai Hong Lui, C. K. M. Lee
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Pediatric Blood &Cancer, EarlyView.ABSTRACT The pediatric hematology‐oncology fellowship training curriculum has not substantially changed since its inception. The first year of training is clinically focused, and the second and third years are devoted to scholarship. However, this current structure leaves many fellows less competitive in the current job market, resulting in ...
Scott C. Borinstein +3 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 of Munich Functional Developmental Diagnosis
Pediatric Reports, 2021 Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life.
Grażyna Pazera +3 more
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N-Dimensional Principal Component Analysis [PDF]
, 2010 In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
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