Results 21 to 30 of about 2,306,590 (284)

Improved Two-Dimensional Quaternion Principal Component Analysis

open access: yesIEEE Access, 2019
The two-dimensional quaternion principal component analysis (2D-QPCA) is first improved into abstracting the features of quaternion matrix samples in both row and column directions, being the generalization ability, and with the components weighted by ...
Meixiang Zhao, Zhigang Jia, Dunwei Gong
doaj   +1 more source

Longitudinal functional principal component analysis [PDF]

open access: yesElectronic Journal of Statistics, 2010
We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, longitudinal (or dynamic) subject-specific variability, subject-visit-specific variability and measurement error. The
Greven, Sonja   +3 more
openaire   +3 more sources

Structured Functional Principal Component Analysis [PDF]

open access: yesBiometrics, 2014
Summary Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics
Shou, Haochang   +3 more
openaire   +4 more sources

Sea surface temperature patterns in the Tropical Atlantic: Principal component analysis and nonlinear principal component analysis

open access: yesTerrestrial, Atmospheric and Oceanic Sciences, 2017
The tropical Atlantic Ocean exhibits several modes of interannual variability such as the equatorial (or Atlantic Niño) mode, and meridional (or Atlantic dipole) mode.
S. C. Kenfack   +6 more
doaj   +1 more source

Bilinear Probabilistic Principal Component Analysis [PDF]

open access: yesIEEE Transactions on Neural Networks and Learning Systems, 2012
Probabilistic principal component analysis (PPCA) is a popular linear latent variable model for performing dimension reduction on 1-D data in a probabilistic manner. However, when used on 2-D data such as images, PPCA suffers from the curse of dimensionality due to the subsequently large number of model parameters.
Kwok, JT, Yu, PLH, Zhao, J
openaire   +4 more sources

Principal Component Analysis of Infertility Data

open access: yesJournal of Kufa for Mathematics and Computer, 2013
This paper applied PCA on infertility set of data, that was collected from Al-Nasiriya  province.  Infertility of  women that have been unable to conceive a child after one year of their marriage without birth control. Since infertility is very common
Nazera Khalil Dakhil   +2 more
doaj   +1 more source

Robust Orthogonal Complement Principal Component Analysis [PDF]

open access: yes, 2016
Recently, the robustification of principal component analysis has attracted lots of attention from statisticians, engineers and computer scientists. In this work we study the type of outliers that are not necessarily apparent in the original observation ...
Li, Shijie, She, Yiyuan, Wu, Dapeng
core   +1 more source

Osmotic dehydration of fish: principal component analysis [PDF]

open access: yesActa 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
doaj   +1 more source

Morphological Principal Component Analysis for Hyperspectral Image Analysis

open access: yesISPRS 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

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