  Mean shift (MS) algorithms are popular methods for mode finding in pattern
analysis. Each MS algorithm can be phrased as a fixed-point iteration scheme,
which operates on a kernel density estimate (KDE) based on some data. The
ability of an MS algorithm to obtain the modes of its KDE depends on whether or
not the fixed-point scheme converges. The convergence of MS algorithms have
recently been proved under some general conditions via first principle
arguments. We complement the recent proofs by demonstrating that the MS
algorithm operating on a Gaussian KDE can be viewed as an MM
(minorization-maximization) algorithm, and thus permits the application of
convergence techniques for such constructions. For the Gaussian case, we extend
upon the previously results by showing that the fixed-points of the MS
algorithm are all stationary points of the KDE in cases where the stationary
points may not necessarily be isolated.
