Results 21 to 30 of about 2,186,647 (234)

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

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

Principal Component Analysis of Infertility Data

Journal 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, Hind Restom Mohammed, Hayder Abood kouree   +2 more
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

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

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

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