An Interpretable and Stable Framework for Sparse Principal Component Analysis
Sparse principal component analysis (SPCA) addresses the poor interpretability and variable redundancy often encountered by principal component analysis (PCA) in high-dimensional data. However, SPCA typically imposes uniform penalties on variables and does not account for differences in variable importance, which may lead to unstable performance in highly noisy or structurally complex settings. We propose SP-SPCA, a method that introduces a single equilibrium parameter into the regularization framework to adaptively adjust variable penalties. This modification of the L2 penalty provides flexible control over the trade-off between sparsity and explained variance while maintaining computati...
| Authors | Ying Hu, Hu Yang |
|---|---|
| Primary category | stat.ML |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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