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 highdimensional 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) <arXiv:1804.00341>.
Package details 


Author  N. Benjamin Erichson, Peng Zheng, and Sasha Aravkin 
Maintainer  N. Benjamin Erichson <[email protected]> 
License  GPL (>= 3) 
Version  0.1.2 
URL  https://github.com/erichson/spca 
Package repository  View on CRAN 
Installation 
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