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 <gl2521@cumc.columbia.edu>, Haocheng Ding <haochengding@ufl.edu>, Jiayi Ji <jj2876@caa.columbia.edu>
MaintainerJiayi Ji <jj2876@caa.columbia.edu>
LicenseMIT + file LICENSE
Version0.3.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("SuperPCA")

Try the SuperPCA package in your browser

Any scripts or data that you put into this service are public.

SuperPCA documentation built on March 13, 2020, 2:09 a.m.