Results 31 to 40 of about 2,186,647 (234)
Decomposable Principal Component Analysis
, 2008 We consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute its computation.
Hero III, Alfred O., Wiesel, Ami
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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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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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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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Euler Principal Component Analysis [PDF]
International Journal of Computer Vision, 2012 Principal Component Analysis (PCA) is perhaps the most prominent learning tool for dimensionality reduction in pattern recognition and computer vision. However, the ℓ 2-norm employed by standard PCA is not robust to outliers. In this paper, we propose a kernel PCA method for fast and robust PCA, which we call Euler-PCA (e-PCA).
Stephan Liwicki +3 more
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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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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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Quantum principal component analysis [PDF]
Nature Physics, 2014 9 pages, Plain ...
Lloyd, Seth +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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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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