Lee Jongmin, Kim Jang-Hyun, Oh Hee-Seok
IEEE Trans Pattern Anal Mach Intell. 2021 Jun;43(6):2165-2171. doi: 10.1109/TPAMI.2020.3025327. Epub 2021 May 11.
This paper presents a new approach for dimension reduction of data observed on spherical surfaces. Several dimension reduction techniques have been developed in recent years for non-euclidean data analysis. As a pioneer work, (Hauberg 2016) attempted to implement principal curves on Riemannian manifolds. However, this approach uses approximations to process data on Riemannian manifolds, resulting in distorted results. This study proposes a new approach to project data onto a continuous curve to construct principal curves on spherical surfaces. Our approach lies in the same line of (Hastie and Stuetzle et al. 1989) that proposed principal curves for data on euclidean space. We further investigate the stationarity of the proposed principal curves that satisfy the self-consistency on spherical surfaces. The results on the real data analysis and simulation examples show promising empirical characteristics of the proposed approach.
本文提出了一种对在球面上观测到的数据进行降维的新方法。近年来,已经为非欧几里得数据分析开发了几种降维技术。作为一项开创性工作,(豪贝格,2016年)试图在黎曼流形上实现主曲线。然而,这种方法使用近似来处理黎曼流形上的数据,导致结果失真。本研究提出了一种将数据投影到连续曲线上以在球面上构建主曲线的新方法。我们的方法与(哈斯蒂和施图茨勒等人,1989年)提出的用于欧几里得空间数据的主曲线方法属于同一思路。我们进一步研究了所提出的主曲线在球面上满足自一致性的平稳性。实际数据分析和模拟示例的结果显示了所提出方法具有良好的经验特征。