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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...
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 ...
