SuperPCA (0.3.0)

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Supervised Principal Component Analysis.

Dimension reduction of complex data with supervision from auxiliary information. The package contains a series of methods for different data types (e.g., multi-view or multi-way data) including the supervised singular value decomposition (SupSVD), supervised sparse and functional principal component (SupSFPC), supervised integrated factor analysis (SIFA) and supervised PARAFAC/CANDECOMP factorization (SupCP). When auxiliary data are available and potentially affect the intrinsic structure of the data of interest, the methods will accurately recover the underlying low-rank structure by taking into account the supervision from the auxiliary data. For more details, see the paper by Gen Li, .

Maintainer: Jiayi Ji
Author(s): Gen Li <>, Haocheng Ding <>, Jiayi Ji <>

License: MIT + file LICENSE

Uses: fBasics, glmnet, MASS, matlab, matlabr, Matrix, matrixStats, pracma, psych, R.matlab, RSpectra, spls, timeSeries

Released about 1 month ago.

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