AI RESEARCH
Deflation-Free Optimal Scoring
arXiv CS.LG
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ArXi:2604.25664v1 Announce Type: cross Sparse Optimal Scoring (SOS) reformulates linear discriminant analysis to enable feature selection through elastic net regularization, making it well-suited for high-dimensional settings where the number of features exceeds observations. Most existing SOS methods use deflation-based strategies that compute discriminant vectors sequentially, which can propagate errors and produce suboptimal solutions.