Under weak spherical symmetry and noisy data, SVD-based selection on the canonical dependence matrix yields asymptotically optimal error exponents up to a residual depending on symmetry deviation and noise levels.
High-dimensional probability
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Universal Feature Selection with Noisy Observations and Weak Symmetry Conditions
Under weak spherical symmetry and noisy data, SVD-based selection on the canonical dependence matrix yields asymptotically optimal error exponents up to a residual depending on symmetry deviation and noise levels.