Presents a unified incremental SVD framework with a projection-based rule for rank-1 updates and systematic comparisons of periodic, error-threshold, angle-threshold, and adaptive refresh policies, claiming near full-SVD accuracy at lower cost on synthetic and ETF data.
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Incremental SVD for Large-Scale Dynamic Matrices: Accuracy, Subspace Stability, Refresh Strategies, and Financial Factor-Based Risk Models
Presents a unified incremental SVD framework with a projection-based rule for rank-1 updates and systematic comparisons of periodic, error-threshold, angle-threshold, and adaptive refresh policies, claiming near full-SVD accuracy at lower cost on synthetic and ETF data.