Mathematics
Random Matrix Theory in Signal Processing
Quick fact
When the number of sensors grows at the same rate as the number of samples, the eigenvalues of the sample covariance matrix do not converge to the true population eigenvalues—they spread according to the Marchenko–Pastur law, even with infinite data.
Why this is interesting
Imagine you're trying to hear a faint whisper in a crowded room using many microphones. You’d expect that more microphones always help—but in high dimensions, the noise itself can fool your equipment. Random matrix theory explains why.