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Near-Optimal Algorithms for Differentially-Private Principal Components
Near-Optimal Algorithms Differentially-Private Principal Components
2012/9/19
Principal components analysis (PCA) is a standard tool for identifying good low-dimensional approximations to data sets in high dimension. Many current data sets of interest contain private or sensiti...
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
...
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 ...
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...
Common functional principal components
Functional principal components nonparametric regression bootstrap two sample problem
2010/3/17
Functional principal component analysis (FPCA) based on the
Karhunen–Lo`eve decomposition has been successfully applied in many
applications, mainly for one sample problems. In this paper we conside...
