English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Directional data consist of observations distributed on a (hyper)sphere, andappear in many applied fields, such as astronomy, ecology, and environmentalscience. This paper studies both statistical and computational problems ofkernel smoothing for directional data. We generalize the classical mean shiftalgorithm to directional data, which allows us to identify local modes of thedirectional kernel density estimator (KDE). The statistical convergence ratesof the directional KDE and its derivatives are derived, and the problem of modeestimation is examined. We also prove the ascending property of the directionalmean shift algorithm and investigate a general problem of gradient ascent onthe unit hypersphere. To demonstrate the applicability of the algorithm, weevaluate it as a mode clustering method on both simulated and real-world datasets.

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data