期刊
ENTROPY
卷 22, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/e22010082
关键词
artificial intelligence; mistake correction; concentration of measure; discriminant; data mining; geometry
资金
- Ministry of Science and Higher Education of the Russian Federation [14.Y26.31.0022]
- Innovate UK (Knowledge Transfer Partnership) [KTP009890, KTP010522]
- University of Leicester
- Spanish Ministry of Economy, Industry, and Competitiveness [FIS2017-82900-P]
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.
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