Universal Feature Selection with Noisy Observations and Weak Symmetry Conditions

Abstract:This paper relaxes the restrictive symmetry conditions adopted in [4], [5] and extends their universal feature selection framework to accommodate noisy observations as well as attribute structures that may exhibit directional preferences. We introduce the notion of weak spherical symmetry, quantified by second-moment distances, which allows controlled deviations from rotational invariance. Under this relaxed condition, we develop a universal feature selection framework based on the singular value decomposition of the canonical dependence matrix computed from noisy data. Our main result shows that the selected features achieve asymptotically optimal error exponents up to a residual term that depends on the symmetry deviation $\delta$ and the noise levels $\eta_1, \eta_2$. When $\delta, \eta_1, \eta_2$ are relatively small, our result recovers that of [5], thereby demonstrating that exact spherical symmetry is unnecessary. Overall, our findings highlight the robustness of the selection framework against second-moment deviations and observation noise, thereby broadening its applicability across diverse inference tasks and providing a theoretically grounded tool for universal feature selection in practical scenarios.

Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)

arXiv:2605.09396 [cs.IT]

(or arXiv:2605.09396v2 [cs.IT] for this version)

https://doi.org/10.48550/arXiv.2605.09396 Focus to learn more arXiv-issued DOI via DataCite

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