We present a rigorous analysis of several popular forms of short memory adaptive
eigenanalysis algorithms using a stochastic averaging method. A first-order analysis shows
that the algorithms do not have any equilibrium points despite published claims to the
contrary. Through averaging analysis, we show that they hover around an appropriate
eigenvector. A second-order analysis is also given without the Gaussian noise assumption,
and our results greatly outperform an earlier approximation in the literature. The second …

… Abstract— We present a rigorous analysis of several popular forms of short memory
adaptive eigenanalysis algorithms using a stochastic averaging method. A first-order
analysis shows that the algorithms do not have any equilibrium points despite published
claims to the contrary. Through averaging …