A PCA-based Data Prediction Method

被引：1

作者：

Daugulis, Peteris ^{[1
]}

Vagale, Vija ^{[1
]}

Mancini, Emiliano ^{[2
,3
]}

Castiglione, Filippo ^{[4
]}

机构：

[1] Daugavpils Univ, Daugavpils, Latvia

[2] Hasselt Univ, Data Sci Inst, Diepenbeek, Belgium

[3] Amsterdam UMC, Dept Global Hlth, Amsterdam, Netherlands

[4] Inst Comp Applicat, Rome, Italy

来源：

BALTIC JOURNAL OF MODERN COMPUTING | 2022年 / 10卷 / 01期

关键词：

D O I：

10.22364/bjmc.2022.10.1.01

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The problem of choosing appropriate values for missing data is often encountered in the data science. We describe a novel method containing both traditional mathematics and machine learning elements for prediction (imputation) of missing data. This method is based on the notion of distance between shifted linear subspaces representing the existing data and candidate sets. The existing data set is represented by the subspace spanned by its first principal components. Solutions for the case of the Euclidean metric are given.

引用

页码：1 / 16

页数：16

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