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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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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
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Osmotic dehydration of fish: principal component analysis [PDF]
Acta Periodica Technologica, 2014 Osmotic treatment of the fish Carassius gibelio was studied in two osmotic solutions: ternary aqueous solution - S1, and sugar beet molasses - S2, at three solution temperatures of 10, 20 and 30oC, at atmospheric pressure. The aim was to examine
Lončar Biljana Lj. +6 more
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Pcadapt: An R Package to Perform Genome Scans for Selection Based on Principal Component Analysis
bioRxiv, 2016 The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure.
Keurcien Luu, Eric Bazin, M. Blum
semanticscholar +1 more source
Parameterized principal component analysis [PDF]
Pattern Recognition, 2018 When modeling multivariate data, one might have an extra parameter of contextual information that could be used to treat some observations as more similar to others. For example, images of faces can vary by age, and one would expect the face of a 40 year old to be more similar to the face of a 30 year old than to a baby face.
Ajay Gupta, Adrian Barbu
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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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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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Robust Principal Component Analysis on Graphs [PDF]
, 2015 Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples.
Bresson, Xavier +4 more
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Quantum principal component analysis [PDF]
Nature Physics, 2014 9 pages, Plain ...
Lloyd, Seth +2 more
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Multiscale principal component analysis
, 2013 Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances.
Akinduko, A. A., Gorban, A. N.
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