Results 31 to 40 of about 2,300,147 (337)

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, Navya Nagananda, Andreas Savakis   +2 more
doaj   +1 more source

HisCoM-PCA: software for hierarchical structural component analysis for pathway analysis based using principal component analysis [PDF]

Genomics & Informatics, 2020
In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results.
Nan Jiang, Sungyoung Lee, Taesung Park
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

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, Fuqing Duan, Zhenkuan Pan, Zhongke Wu, Jinhua Li, Qingqiong Deng, Xiaona Li, Mingquan Zhou   +7 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

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

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

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

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

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, Wang, Liangliang, Cao, Jiguo   +2 more
openaire   +2 more sources