An R package for testing high-dimensional covariance matrices
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Updated
Oct 5, 2017 - R
An R package for testing high-dimensional covariance matrices
Gradient-based Fantope projection and selection algorithm for sparse PCA
Out-of-sample volatility forecasting and Value-at-Risk backtesting for 14 currencies (2000–2026): GARCH/EGARCH/GJR vs. RiskMetrics, with QLIKE and Diebold-Mariano model comparison, Kupiec/Christoffersen VaR coverage tests, and sparse PCA on FX returns. Python.
PCA, sparse PCA, and manual penalized matrix decomposition of FIFA 17 Eredivisie player skills, linked to position and market value.
TopoSPCA: Topology-Dependent Robustness in Graph-Regularized Sparse PCA
An R Package for Sparse PCA with Multiple Principal Components
Scripts for the paper: Robust Sparse Smooth Principal Component Analysis for Face Reconstruction and Recognition
Scripts for the paper: Robust Sparse Smooth Principal Component Analysis for Face Reconstruction and Recognition
Subspace projection methods (PCA, SPCA, SSPCA, KernelPCA, Isomap) for visual classification. Structured Sparse PCA with co-clustering outperforms baselines on VOC and Caltech benchmarks. Published at BigMM 2017.
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