Title: Factor Analysis for Spectral Estimation,
Abstract: Power spectrum estimation is an important tool in many applications, such as
the whitening of noise. The popular multitaper method enjoys significant
success, but fails for short signals with few samples. We propose a statistical
model where a signal is given by a random linear combination of fixed, yet
unknown, stochastic sources. Given multiple such signals, we estimate the
subspace spanned by the power spectra of these fixed sources. Projecting
individual power spectrum estimates onto this subspace increases estimation
accuracy. We provide accuracy guarantees for this method and demonstrate it on
simulated and experimental data from cryo-electron microscopy.