Linear convergence of the subspace constrained mean shift algorithm: from Euclidean to directional data.
Inf inference
; 12(1): 210-311, 2023 Mar.
Article
en En
| MEDLINE
| ID: mdl-36761435
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Inf inference
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos