AI RESEARCH
An Interpretable and Stable Framework for Sparse Principal Component Analysis
arXiv CS.LG
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ArXi:2603.13806v1 Announce Type: cross 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