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A Derivation of the Algebraic Curve for Multi-dimensional Data using the Least-squares Distance

Masahiro Mizuta

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Abstract

We propose a method for finding the algebraic curve that fits multi-dimensional data. An algebraic curve in n dimensional space is generally defined by n-1 polynomial expressions. The proposed method finds the n-1 polynomial expressions of the algebraic curve for n dimensional data. The sum of the squares' distances from the data point to the nearest point on the curve is minimum.

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What this paper is about

We propose a method for finding the algebraic curve that fits multi-dimensional data. An algebraic curve in n dimensional space is generally defined by n-1 polynomial expressions. The proposed method finds the n-1 polynomial expressions of the algebraic curve for n dimensional data. The sum of the squares' distances from the data point to the nearest point on the curve is minimum.

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Available abstract

We propose a method for finding the algebraic curve that fits multi-dimensional data. An algebraic curve in n dimensional space is generally defined by n-1 polynomial expressions. The proposed method finds the n-1 polynomial expressions of the algebraic curve for n dimensional data. The sum of the squares' distances from the data point to the nearest point on the curve is minimum.

Key concepts: Stable curve, Mathematics, Algebraic curve, Curve fitting, Real algebraic geometry, Singular point of an algebraic variety, Polynomial, Algebraic number

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