Scale-Invariant, Robust Sparse PCA for Large Data via Differentiable Penalties
Sparse PCA finds low-dimensional structure that loads on few features. Existing methods couple learning and feature selection by applying non-differentiable penalties that force retain-or-zero decisions during optimization. This eliminates features before the optimizer has established which ones matter, limits scalabil...