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sparsepca  

Sparse Principal Component Analysis (SPCA)
View on CRAN: Click here


Download and install sparsepca package within the R console
Install from CRAN:
install.packages("sparsepca")

Install from Github:
library("remotes")
install_github("cran/sparsepca")

Install by package version:
library("remotes")
install_version("sparsepca", "0.1.2")



Attach the package and use:
library("sparsepca")
Maintained by
N. Benjamin Erichson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-04-11
Latest Update: 2018-04-11
Description:
Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <doi:10.48550/arXiv.1804.00341>.
How to cite:
N. Benjamin Erichson (2018). sparsepca: Sparse Principal Component Analysis (SPCA). R package version 0.1.2, https://cran.r-project.org/web/packages/sparsepca. Accessed 07 Nov. 2024.
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Complete documentation for sparsepca
Functions, R codes and Examples using the sparsepca R package
Some associated functions: robspca . rspca . spca . 
Some associated R codes: robspca.R . rspca.R . spca.R .  Full sparsepca package functions and examples
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