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
收藏
页码：1 / 16
页数：16
相关论文
共 50 条
  • [31] A PCA-based method for ancestral informative markers selection in structured populations
    Feng Zhang
    Lei Zhang
    Hong-Wen Deng
    Science in China Series C: Life Sciences, 2009, 52 : 972 - 976
  • [32] A PCA-based Generalized Multifactor Reduction Method for Correcting Population Stratification
    Lou, Xiang-Yang
    Chen, Guo-Bo
    Yan, Lei
    Liu, Nianjun
    Klimentidis, Yann C.
    Zhu, Xiaofeng
    Zhi, Degui
    Wang, Xujing
    GENETIC EPIDEMIOLOGY, 2012, 36 (07) : 753 - 753
  • [33] On PCA-based fault diagnosis techniques
    Yin, Shen
    Ding, Steven X.
    Naik, Amol
    Deng, Pengcheng
    Haghani, Adel
    2010 CONFERENCE ON CONTROL AND FAULT-TOLERANT SYSTEMS (SYSTOL'10), 2010, : 179 - 184
  • [34] Chunk-wise regularised PCA-based imputation of missing data
    A. Iodice D’Enza
    A. Markos
    F. Palumbo
    Statistical Methods & Applications, 2022, 31 : 365 - 386
  • [35] Enhanced PCA-Based Localization Using Depth Maps with Missing Data
    Carreira, Fernando
    Calado, Joao M. F.
    Cardeira, Carlos
    Oliveira, Paulo
    JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2015, 77 (02) : 341 - 360
  • [36] Development of a structuring residuals method for diagnostic by PCA-based genetic algorithms
    Najeh, Tawfik
    Nabli, Lotfi
    2012 2ND INTERNATIONAL CONFERENCE ON COMMUNICATIONS, COMPUTING AND CONTROL APPLICATIONS (CCCA), 2012,
  • [37] PCA-BASED HUMAN POSTURE CLASSIFICATION
    Tahir, Nooritawati Md
    Hussain, Aini
    Samad, Salina Abdul
    Husain, Hafizah
    JURNAL TEKNOLOGI, 2007, 46
  • [38] A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method
    Hu, Yunpeng
    Chen, Huanxin
    Li, Guannan
    Li, Haorong
    Xu, Rongji
    Li, Jiong
    ENERGY AND BUILDINGS, 2016, 112 : 270 - 278
  • [39] PCAH: a PCA-based Hierarchical Clustering Method for Visual Words Construction
    He, Ying
    Wang, Jian
    Zhong, Xue-xia
    Mei, Lin
    Wu, Zhi-zong
    2015 15TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING, 2015, : 1009 - 1018
  • [40] A PCA-BASED METHOD FOR REMANUFACTURING PROCESS OPTIMIZATION FROM SUSTAINABILITY ASPECTS
    Shi, Junli
    Xu, Huanhuan
    Shu, Fangli
    Ren, Mengmeng
    Lu, Zhongchi
    INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE, 2023, 30 (04): : 986 - 998
12345