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  "Description": "Sparse principal component analysis (SPCA) attempts to\nfind sparse weight vectors (loadings), i.e., a weight vector\nwith only a few 'active' (nonzero) values. This approach\nprovides better interpretability for the principal components\nin high-dimensional data settings. This is, because the\nprincipal components are formed as a linear combination of only\na few of the original variables. This package provides\nefficient routines to compute SPCA. Specifically, a variable\nprojection solver is used to compute the sparse solution. In\naddition, a fast randomized accelerated SPCA routine and a\nrobust SPCA routine is provided. Robust SPCA allows to capture\ngrossly corrupted entries in the data.",
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