SuperPCA: 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, <DOI:10.1111/biom.12698>.

Getting started

Package details

AuthorGen Li <>, Haocheng Ding <>, Jiayi Ji <>
MaintainerJiayi Ji <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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SuperPCA documentation built on July 26, 2021, 5:06 p.m.