Results 21 to 30 of about 2,126,918 (327)

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

Terrestrial, 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, K. F. Mkankam, G. Alory, Y. du Penhoat, N. M. Hounkonnou, D. A. Vondou, G. B. Nfor   +6 more
doaj   +1 more source

Improved Two-Dimensional Quaternion Principal Component Analysis

IEEE 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

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., Pezo Lato L., Lević Ljubinko B., Filipović Vladimir S., Nićetin Milica R., Knežević Violeta M., Kuljanin Tatjana A.   +6 more
doaj   +1 more source

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

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, Bronstein, Michael, Kalofolias, Vassilis, Shahid, Nauman, Vandergheynst, Pierre   +4 more
core   +2 more sources

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, Georgios Tzimiropoulos, Stefanos Zafeiriou, Maja Pantic   +3 more
openaire   +4 more sources

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

Robust Orthogonal Complement Principal Component Analysis [PDF]

, 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