Using the Robust Principal Component Analysis to Identify Incorrect Aerological Data

被引：0
|
作者：
A. M. Kozin
A. D. Lykov
I. A. Vyazankin
A. S. Vyazankin
机构：
[1] Central Aerological Observatory,
[2] Sechenov First Moscow State Medical University,undefined
来源：
Russian Meteorology and Hydrology | 2021年 / 46卷
关键词：
aerological data; geopotential height; outliers; machine learning; Robust Principal Component Analysis (RPCA);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：631 / 639
页数：8
相关论文
共 50 条
  • [41] An automatic robust Bayesian approach to principal component regression
    Gagnon, Philippe
    Bedard, Mylene
    Desgagne, Alain
    JOURNAL OF APPLIED STATISTICS, 2021, 48 (01) : 84 - 104
  • [42] A New Perspective on Principal Component Analysis using Inverse Covariance
    Gulrez, Tauseef
    Al-Odienat, Abdullah
    INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2015, 12 (01) : 104 - 109
  • [43] A Robust Principal Component Analysis Approach to DoS-Related Network Anomaly Detection
    Doucette, Cody
    Broderick-Sander, Regan
    Toll, Benjamin
    Helsinger, Aaron
    Soule, Nathaniel
    Pal, Partha
    Zhou, Chong
    Paffenroth, Randy
    CYBER SENSING 2020, 2020, 11417
  • [44] Principal component analysis of turbulent combustion data: Data pre-processing and manifold sensitivity
    Parente, Alessandro
    Sutherland, James C.
    COMBUSTION AND FLAME, 2013, 160 (02) : 340 - 350
  • [45] Prediction Model based on Continuous Data for Student Performance using Principal Component Analysis and Support Vector Machine
    Sabri, Mohammad Zahid Mohammad
    Majid, Nazatul Aini Abd
    Hanawi, Siti Aishah
    Talib, Nur Izzati Mohd
    Yatim, Ariff Imran Anuar
    TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS, 2023, 12 (02): : 1201 - 1210
  • [46] Comparison of Machine Learning Classifiers for dimensionally reduced fMRI data using Random Projection and Principal Component Analysis
    Suhaimi, Nur Farahana Mohd
    Htike, Zaw Zaw
    2019 7TH INTERNATIONAL CONFERENCE ON MECHATRONICS ENGINEERING (ICOM), 2019, : 7 - 12
  • [47] Robust supervised probabilistic principal component analysis model for soft sensing of key process variables
    Zhu, Jinlin
    Ge, Zhiqiang
    Song, Zhihuan
    CHEMICAL ENGINEERING SCIENCE, 2015, 122 : 573 - 584
  • [48] A Method of L1-Norm Principal Component Analysis for Functional Data
    Yu, Fengmin
    Liu, Liming
    Yu, Nanxiang
    Ji, Lianghao
    Qiu, Dong
    SYMMETRY-BASEL, 2020, 12 (01):
  • [49] L1-Norm Principal-Component Analysis of Complex Data
    Tsagkarakis, Nicholas
    Markopoulos, Panos P.
    Sklivanitis, George
    Pados, Dimitris A.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2018, 66 (12) : 3256 - 3267
  • [50] Robust simultaneous confidence interval estimation of principal component loadings
    Borlongan, Martin Augustine B.
    Lansangan, Joseph Ryan G.
    Barrios, Erniel B.
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2019, 48 (02) : 431 - 446
12345