Sparse Principal Component Analysis (SPCA)

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) .


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0.1.2 by N. Benjamin Erichson, 3 years ago

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Authors: N. Benjamin Erichson , Peng Zheng , and Sasha Aravkin

Documentation:   PDF Manual  

GPL (>= 3) license

Imports rsvd

Imported by SparseBiplots, categoryEncodings.

See at CRAN