Cai Tony, Fan Jianqing, Jiang Tiefeng
Statistics Department The Wharton School University of Pennsylvania Philadelphia, PA 19104, USA
Department of Operation Research and Financial Engineering Princeton University Princeton, NJ 08540, USA
J Mach Learn Res. 2013 Jan;14(1):1837-1864.
This paper studies the asymptotic behaviors of the pairwise angles among randomly and uniformly distributed unit vectors in [Formula: see text] as the number of points → ∞, while the dimension is either fixed or growing with . For both settings, we derive the limiting empirical distribution of the random angles and the limiting distributions of the extreme angles. The results reveal interesting differences in the two settings and provide a precise characterization of the folklore that "all high-dimensional random vectors are almost always nearly orthogonal to each other". Applications to statistics and machine learning and connections with some open problems in physics and mathematics are also discussed.
本文研究了在(\mathbb{R}^d)中随机且均匀分布的单位向量之间两两夹角的渐近行为,其中点的数量趋于无穷,而维度(d)要么固定,要么随(n)增长。对于这两种情况,我们推导了随机角度的极限经验分布以及极端角度的极限分布。结果揭示了这两种情况中有趣的差异,并对“所有高维随机向量几乎总是几乎相互正交”这一民间说法给出了精确的描述。还讨论了在统计和机器学习中的应用以及与物理和数学中一些开放问题的联系。