Reservoir Subspace Injection for Online ICA under Top-n Whitening
This work addresses a specific bottleneck in online ICA for signal processing applications, offering an incremental improvement with diagnostic tools.
The paper tackles the problem of online independent component analysis (ICA) under nonlinear mixing, where reservoir expansion can degrade performance due to top-n whitening discarding injected features. The result is a guarded reservoir subspace injection controller that recovers mean performance to within 0.1 dB of baseline and improves over vanilla online ICA by +1.7 dB under nonlinear mixing.
Reservoir expansion can improve online independent component analysis (ICA) under nonlinear mixing, yet top-$n$ whitening may discard injected features. We formalize this bottleneck as \emph{reservoir subspace injection} (RSI): injected features help only if they enter the retained eigenspace without displacing passthrough directions. RSI diagnostics (IER, SSO, $ρ_x$) identify a failure mode in our top-$n$ setting: stronger injection increases IER but crowds out passthrough energy ($ρ_x: 1.00\!\rightarrow\!0.77$), degrading SI-SDR by up to $2.2$\,dB. A guarded RSI controller preserves passthrough retention and recovers mean performance to within $0.1$\,dB of baseline $1/N$ scaling. With passthrough preserved, RE-OICA improves over vanilla online ICA by $+1.7$\,dB under nonlinear mixing and achieves positive SI-SDR$_{\mathrm{sc}}$ on the tested super-Gaussian benchmark ($+0.6$\,dB).