Signal Recovery Using a Spiked Mixture Model
Published in preprint, 2025
We introduce the spiked mixture model (SMM) to address the problem of estimating a set of signals from many randomly scaled and noisy observations. Subsequently, we design a novel expectation-maximization (EM) algorithm to recover all parameters of the SMM. Numerical experiments show that in low signal-to-noise ratio regimes, and for data types where the SMM is relevant, SMM surpasses the more traditional Gaussian mixture model (GMM) in terms of signal recovery performance.
Low noise \(\sigma^2 = 0.01\)
High noise \(\sigma^2 = 0.5\)
