Gorban Alexander N, Makarov Valery A, Tyukin Ivan Y
Department of Mathematics, University of Leicester, Leicester LE1 7RH, UK.
Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, 603022 Nizhny Novgorod, Russia.
Entropy (Basel). 2020 Jan 9;22(1):82. doi: 10.3390/e22010082.
High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning. The well-known phenomenon of the "curse of dimensionality" states: many problems become exponentially difficult in high dimensions. Recently, the other side of the coin, the "blessing of dimensionality", has attracted much attention. It turns out that generic high-dimensional datasets exhibit fairly simple geometric properties. Thus, there is a fundamental tradeoff between complexity and simplicity in high dimensional spaces. Here we present a brief explanatory review of recent ideas, results and hypotheses about the blessing of dimensionality and related simplifying effects relevant to machine learning and neuroscience.
高维数据以及现实的高维表示是现代人工智能系统和机器学习应用的固有特征。著名的“维度诅咒”现象表明:在高维空间中,许多问题会变得呈指数级困难。最近,事情的另一面,即“维度之福”,引起了广泛关注。事实证明,一般的高维数据集呈现出相当简单的几何特性。因此,在高维空间中,复杂性和简单性之间存在着一种基本的权衡。在此,我们简要解释性地综述一下关于维度之福以及与机器学习和神经科学相关的简化效应的近期观点、成果和假设。