搜索结果: 1-9 共查到“principal components analysis”相关记录9条 . 查询时间(0.125 秒)
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of
data in the form of n observations (or cases) of a vector with p variables. Contemporary data sets
of...
ON THE DISTRIBUTION OF THE LARGEST EIGENVALUE IN PRINCIPAL COMPONENTS ANALYSIS
LARGEST EIGENVALUE PRINCIPAL COMPONENTS
2015/8/20
Let x1 denote the square of the largest singular value of an n × p
matrix X, all of whose entries are independent standard Gaussian varates. Equivalently, x1 is the largest principal component vari...
Classification of Multi-spectral, Multi-temporal And Multi-sensor Images Using Principal Components Analysis and Artificial Neural Networks: Beykoz Case
Land Cover Classification Artificial Neural Networks
2015/7/9
The thematic maps derived from remotely-sensed images are invaluable sources of information for various investigations since they
provide spatial and temporal information about the nature of Earth su...
Covariate adjusted functional principal components analysis for longitudinal data
Functional data analysis functional principal componentsanalysis local linear regression longitudinal data analysis smoothing sparse data
2010/3/11
Classical multivariate principal component analysis has been extended
to functional data and termed functional principal component
analysis (FPCA). Most existing FPCA approaches do not accommodate
...
Interpretation of Water Quality Data by Principal Components Analysis
Multivariate analysis principal components analysis quality variables river-basin
2009/10/14
A variety of methods are being used to display the information which is concealed in the quality variables observed in a water quality monitoring network. A large portion of these approaches are stati...
Functional principal components analysis via penalized rank one approximation
Functional data analysis penalization regularization singular value decomposition
2009/9/16
Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in ...
Factors influencing fluffy layer suspended matter (FLSM) properties in the Odra River - Pomeranian Bay - Arkona Deep System (Baltic Sea) as derived by principal components analysis (PCA), and cluster analysis (CA)
fluffy layer suspended matter Odra River Arkona Deep System principal components analysis cluster analysis
2009/5/18
Factors conditioning formation and properties of suspended matter resting on the sea floor (Fluffy Layer Suspended Matter - FLSM) in the Odra river mouth - Arkona Deep system (southern Baltic Sea) wer...
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. Contemporary data sets ofte...
Associations between markers of subclinical atherosclerosis and dietary patterns derived by principal components analysis and reduced rank regression in the Multi-Ethnic Study of Atherosclerosis (MESA) 1,2,3
Dietary patterns principal components analysis reduced rank regression carotid artery intima media thickness coronary artery calcium
2008/12/12
Background: The association between diet and cardiovascular disease (CVD) may be mediated partly through inflammatory processes and reflected by markers of subclinical atherosclerosis.Objective: We in...