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>.
| Version: |
0.4.0 |
| Depends: |
Matrix |
| Imports: |
RSpectra, psych, fBasics, R.matlab, glmnet, MASS, matrixStats, timeSeries, stats, matlabr, spls, pracma, matlab |
| Published: |
2021-07-26 |
| Author: |
Gen Li, Haocheng Ding, Jiayi Ji |
| Maintainer: |
Jiayi Ji <jj2876 at caa.columbia.edu> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| CRAN checks: |
SuperPCA results |
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