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首页> 外文期刊>Electronics and Communications in Japan. Part 3, Fundamental Electronic Science >Recent Progress on Geometric Algorithms for Approximating Functions: Toward Applications to Data Analysis
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Recent Progress on Geometric Algorithms for Approximating Functions: Toward Applications to Data Analysis

机译：逼近函数的几何算法的最新进展：面向数据分析的应用

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摘要

Data simplification is an extremely important issue in our current information-oriented society. Normally, a real-world database contains a massive amount of raw data, and when we consider the data as a distribution function, it has fluctuations due to sampling errors, outliers, and/or invalid inputs. Therefore, for data analysis technology such as data mining, it is important to approximate the input data by a simplified function. There are various approaches to function approximation, and functional analytical methods and learning-based techniques are quite popular. Apart from them, computational geometric approach based on optimization using discrete algorithms is widely studied. However, the conventional application of computational geometrical techniques is pattern matching, and to apply them to data analysis, their formulation and optimization criteria must be changed accordingly. Therefore, various difficulties and computational barriers arise, which must be eliminated or avoided. In this paper, we discuss data approximation in computational geometry and describe current trends centered on the author's latest research.
机译：在我们当前的信息社会中，数据简化是一个极其重要的问题。通常，真实世界的数据库包含大量原始数据，当我们将数据视为分布函数时，由于采样错误，离群值和/或无效输入，它会产生波动。因此，对于诸如数据挖掘之类的数据分析技术，通过简化函数近似输入数据很重要。函数逼近有多种方法，并且函数分析方法和基于学习的技术非常流行。除此之外，人们还广泛研究了基于离散算法优化的计算几何方法。但是，计算几何技术的常规应用是模式匹配，并且要将它们应用于数据分析，必须相应地更改其公式和优化标准。因此，出现各种困难和计算障碍，必须消除或避免。在本文中，我们讨论计算几何中的数据近似，并以作者的最新研究为中心描述当前趋势。

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