Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data
Abstract
This paper studies the linear convergence of the subspace constrained mean shift (SCMS) algorithm, a well-known algorithm for identifying a density ridge defined by a kernel density estimator. By arguing that the SCMS algorithm is a special variant of a subspace constrained gradient ascent (SCGA) algorithm with an adaptive step size, we derive the linear convergence of such SCGA algorithm. While the existing research focuses mainly on density ridges in the Euclidean space, we generalize density ridges and the SCMS algorithm to directional data. In particular, we establish the stability theorem of density ridges with directional data and prove the linear convergence of our proposed directional SCMS algorithm.
Cite
@article{arxiv.2104.14977,
title = {Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data},
author = {Yikun Zhang and Yen-Chi Chen},
journal= {arXiv preprint arXiv:2104.14977},
year = {2025}
}
Comments
Substantial revision. The updated version has 93 pages, 12 figures, and 1 table