Projection error evaluation for large multidimensional data sets
Articles
Kotryna Paulauskienė
Vilnius University
Olga Kurasova
Vilnius University
Published 2016-01-20
https://doi.org/10.15388/NA.2016.1.6
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Keywords

dimensionality reduction
projection error
large data set
representative sample

How to Cite

Paulauskienė K. and Kurasova O. (2016) “Projection error evaluation for large multidimensional data sets”, Nonlinear Analysis: Modelling and Control, 21(1), pp. 92-102. doi: 10.15388/NA.2016.1.6.

Abstract

This research deals with projection error evaluation for large data sets using only a personal computer without any particular technologies for high performance computing. A shortcoming of basic projection error calculation ways is such that they require a large amount of computer memory or computation time is not acceptable when large data sets are analyzed. This paper proposes two ways for projection error evaluation: the first one is based on calculating the projection error for not full data set, but only for representative data sample, the second one obtains the projection error by dividing a data set into the smaller data sets. The experiments have been carried out with twelve real and artificial data sets. The computational efficiency of the projection error evaluation ways is confirmed by a comprehensive set of comparisons. We demonstrate that dividing data set into the smaller data sets allows us to calculate the projection error for large data sets.

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