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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...
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...
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...
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 ...
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...
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 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...
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...

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