Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables
Principal Components Analysis (PCA) is a variable reduction technique helps to reduce a complex dataset to a lower dimensional subspace. This study is interested to investigate an approach for handling a problem occurred from considering a very large number of measured variables followed by a classi...
Saved in:
Main Authors: | Hamid, Hashibah, Zainon, Fatinah, Tan, Pei Yong |
---|---|
Format: | Article |
Language: | English |
Published: |
Medwell Publishing
2016
|
Subjects: | |
Online Access: | http://repo.uum.edu.my/21553/1/RJAS%2011%2011%202016%201422-1426.pdf http://repo.uum.edu.my/21553/ https://www.medwelljournals.com/abstract/?doi=rjasci.2016.1422.1426 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Variable extractions using principal component analysis and multiple correspondence analysis for large number of mixed variables classification problems
by: Hamid, Hashibah, et al.
Published: (2016) -
Face biometrics based on principal component analysis and linear discriminant analysis
by: Shaikh Salleh, Sheikh Hussain, et al.
Published: (2010) -
Variables extraction on large binary variables in discriminant analysis based on mixed variables location model
by: Long, Mei Mei, et al.
Published: (2015) -
Using principal component analysis to extract mixed variables for smoothed location model
by: Hamid, Hashibah, et al.
Published: (2013) -
A new approach for classifying large number of mixed variables
by: Hamid, Hashibah
Published: (2010)