Results 21 to 30 of about 408,145 (290)

KPCA over PCA to assess urban resilience to floods [PDF]

E3S Web of Conferences, 2021
Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to ...
Satour Narjiss, Benyacoub Badreddine, El Mahrad Badr, Kacimi Ilias   +3 more
doaj   +1 more source

Principal Component Analysis in ECG Signal Processing

EURASIP Journal on Advances in Signal Processing, 2007
This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loève transform is explained.
Roig José Millet, Laguna Pablo, Bollmann Andreas, Sörnmo Leif, Castells Francisco   +4 more
doaj   +2 more sources

Multivariate classification of provinces of Vietnam according to the level of sustainable development

Bulletin of Geography. Socio-Economic Series, 2021
The research aims to classify the level of sustainability of 63 provinces in Vietnam upon 24 indicators reflecting three main dimensions of sustainable development by using multivariate classification method for the year 2014–2016.
Truong Van Canh
doaj   +1 more source

Fault Diagnosis of Industrial Process Based on FDKICA-PCA

Journal of Harbin University of Science and Technology, 2018
Because the dynamic characteristics of autocorrelation and lag correlation in production process are neglected in fault diagnosis,Kernel Independent Component AnalysisPrincipal Component Analysis (KICAPCA) is very poor in detecting small and gradual ...
ZHANG Jing, ZHU Fei-fei, LIU Jia-xing, WANG Jiang-tao   +3 more
doaj   +1 more source

Improved Algorithms for the Classification of Rough Rice Using a Bionic Electronic Nose Based on PCA and the Wilks Distribution

Sensors, 2014
Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA.
Sai Xu, Zhiyan Zhou, Huazhong Lu, Xiwen Luo, Yubin Lan   +4 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

Association Between Finger-to-Nose Kinematics and Upper Extremity Motor Function in Subacute Stroke: A Principal Component Analysis

Frontiers in Bioengineering and Biotechnology, 2021
BackgroundKinematic analysis facilitates interpreting the extent and mechanisms of motor restoration after stroke. This study was aimed to explore the kinematic components of finger-to-nose test obtained from principal component analysis (PCA) and the ...
Ze-Jian Chen, Ze-Jian Chen, Chang He, Nan Xia, Nan Xia, Ming-Hui Gu, Ming-Hui Gu, Yang-An Li, Yang-An Li, Cai-Hua Xiong, Jiang Xu, Jiang Xu, Xiao-Lin Huang, Xiao-Lin Huang   +13 more
doaj   +1 more source

Urban water quality evaluation using multivariate analysis [PDF]

Acta Montanistica Slovaca, 2007
A data set, obtained for the sake of drinking water quality monitoring, was analysed by multivariate methods. Principal component analysis (PCA) reduced the data dimensionality from 18 original physico-chemical and microbiological parameters determined ...
Petr Praus
doaj  

Determining Principal Component Cardinality through the Principle of Minimum Description Length

, 2019
PCA (Principal Component Analysis) and its variants areubiquitous techniques for matrix dimension reduction and reduced-dimensionlatent-factor extraction. One significant challenge in using PCA, is thechoice of the number of principal components.
A Blumer, AJ Donald, AP Dawid, AR Barron, C Eckart, DC Hoyle, IT Jolliffe, J Josse, J Rissanen, J Rissanen, J Rissanen, J Rissanen, JI Myung, M Mitzenmacher, M Zhu, MH Hansen, T Hastie, TM Cover, Y Choi   +18 more
core   +1 more source

Visualizing genetic constraints [PDF]

, 2013
Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for ...
Gaydos, Travis L., Heckman, Nancy E., Kingsolver, Joel, Kirkpatrick, Mark, Marron, J. S., Schmitt, Johanna, Stinchcombe, J. R.   +6 more
core   +3 more sources