Results 31 to 40 of about 2,261,156 (339)

Ensemble Principal Component Analysis

IEEE Access
Efficient 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, Aleksandr Y. Aravkin, J. Nathan Kutz   +2 more
doaj   +1 more source

Morphological Principal Component Analysis for Hyperspectral Image Analysis

ISPRS International Journal of Geo-Information, 2016
This article deals with the issue of reducing the spectral dimension of a hyperspectral image using principal component analysis (PCA). To perform this dimensionality reduction, we propose the addition of spatial information in order to improve the ...
Gianni Franchi, Jesús Angulo
doaj   +1 more source

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
core   +1 more source

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, Jesus M. Martí-Gavliá, M. Teresa Sebastiá, Jesus Mengual   +3 more
doaj   +1 more source

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, Salvador Torra Porras, Enric Monte Moreno   +2 more
doaj   +1 more source

Principal Component Analysis Of Synthetic Galaxy Spectra [PDF]

, 1998
We analyse synthetic galaxy spectra from the evolutionary models of Bruzual&Charlot and Fioc&Rocca-Volmerange using the method of Principal Component Analysis (PCA).
Alfonso Aragón-Salamanca, Bailer-Jones, Bromley, Bruzual, Bruzual, Bruzual, Colless, Connolly, Everson, Fioc, Folkes, Folkes, Francis, Fukugita, Galaz, Glazebrook, Gunn, Gunn, Kennicutt, Leitherer, Lejeune, Mathis, Murtagh, Ofer Lahav, Osterbrock, Scalo, Shai Ronen, Sodré, Tinsley, Worthey   +29 more
core   +3 more sources

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
doaj   +1 more source

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, Görtler, Jochen, Spinner, Thilo, Streeb, Dirk, Weiskopf, Daniel   +4 more
core   +1 more source

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  

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
core   +1 more source