Improved robust principal component analysis based on minimum regularized covariance determinant for the detection of high leverage points in high dimensional data (penambahbaikan analisis komponen utama berdasarkan penentu kovarian teratur minimum bagi mengecam titik tuasan tinggi untuk data dimensi tinggi)

Improved robust principal component analysis based on minimum regularized covariance determinant for the detection of high leverage points in high dimensional data (penambahbaikan analisis komponen utama berdasarkan penentu kovarian teratur minimum bagi mengecam titik tuasan tinggi untuk data dimensi tinggi)

This paper presents an extension work of robust principal component analysis (ROBPCA) denoted as IRPCA, to improve the accuracy of the detection of high leverage points (HLPs) in high dimensional data (HDD). The IRPCA employs the Principal Component Analysis (PCA) to reduce the dimension of the data...

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Bibliographic Details
Main Authors: Midi, Habshah, Suhaiza, Jaaz, Mohd Aslam, ., Hani Syahida, ., Emi Amielda, .
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2025
Online Access:http://psasir.upm.edu.my/id/eprint/120869/
http://psasir.upm.edu.my/id/eprint/120869/1/120869.pdf

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