A new method of Multi Dimensional Scaling

G. Massini, Stefano Terzi, M. Buscema
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引用次数: 3

Abstract

This paper presents a new algorithm called “Population” that is an efficient and high speed method of performing Multi Dimensional Scaling based only on the calculation of a local fitness. Population is not bound to a specific Cost Function but is possible to define its in relation to the considered objective. The motivation for its creation was for use in the elaboration of datasets of great dimensions. In performance comparisons between Population and the Sammon method, Population has consistently excelled. Because of the nature of the algorithm, it is not necessary for the data set to be complete at the moment of the elaboration, for new data can be introduced dynamically in the system.
一种新的多维标度方法
本文提出了一种新的算法“Population”,它是一种仅基于局部适应度计算的高效、高速的多维缩放方法。人口不受特定成本函数的约束,但可以根据所考虑的目标来定义其成本函数。创建它的动机是为了在大维度的数据集的细化中使用。在Population和Sammon方法的性能比较中,Population一直表现出色。由于算法的性质,在细化的时候并不需要数据集是完整的,因为新的数据可以在系统中动态引入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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