The high dimensionality of spectral data makes even tiny distributional differences from noise or artifacts perfectly separable by ML models, as explained by the Feldman-Hajek theorem and concentration of measure.
Equivalence and perpendicularity of gaussian processes
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
The Infinite-Dimensional Nature of Spectroscopy and Why Models Succeed, Fail, and Mislead
The high dimensionality of spectral data makes even tiny distributional differences from noise or artifacts perfectly separable by ML models, as explained by the Feldman-Hajek theorem and concentration of measure.