msPCA: An R Package for Sparse PCA with Multiple Components

arXiv:2607.05229 · stat.ML, cs.LG, stat.ME · Submitted 2026-07-06 · Read on arXiv

Ryan Cory-Wright, Jean Pauphilet

stat.ML, cs.LG, stat.ME

Submitted: 2026-07-06

Project page: https://jeanpauphilet.github.io/msPCA

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Sparsity — 40× in this paper · explained in 5 episodes
• Redundancy — 11× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Composition — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Global Industry Classification Standard — 2× in this paper
• Interpretability — 2× in this paper · explained in 15 episodes
• Orthogonal Projection — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Comprehensive R Archive Network — 1× in this paper
• Computational Biology — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Riemannian Optimization — 1× in this paper · explained in 3 episodes
• Stiefel Manifold — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• Scalable Posterior Uncertainty for Flexible Density-Based Clustering
• A Transport Geometry of Benign Overfitting
• Efficient quadratic entropy with distance sketches