A new R package named spca has been developed to facilitate the computation of least squares sparse principal components (LS-SPCA). This package offers a framework for generating uncorrelated sparse principal components (sPCs) that maximize explained variance while maintaining strong correlations with standard principal components (PCs). The spca package features an efficient C++ backend for matrix computations and a flexible R frontend, providing users with various options for sparsification and variable selection. AI
IMPACT This package offers a computationally efficient alternative for computing interpretable sparse principal components, potentially aiding in feature selection and dimensionality reduction in machine learning workflows.
RANK_REASON The cluster describes a new R package for statistical computation, which falls under research.
- C++
- Giovanni Maria Merola
- Least Squares Sparse Principal Component Analysis
- LS-SPCA
- R
- sparse principal components
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