Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis
Nonlinear Principal Component Analysis (PRINCALS) is an extension of Principal Component Analysis (Linear), which can reduce the variables of mixed scale multivariable data (nominal, ordinal, interval, and ratio) simultaneously. This study investigated variance the estimation eigen value of Principa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/01/itmconf_iicma2024_04001.pdf |
| _version_ | 1827372010253058048 |
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| author | Makkulau Tenri Ampa Andi Yahya Irma La Ome Lilis Saidi La Ode |
| author_facet | Makkulau Tenri Ampa Andi Yahya Irma La Ome Lilis Saidi La Ode |
| author_sort | Makkulau |
| collection | DOAJ |
| description | Nonlinear Principal Component Analysis (PRINCALS) is an extension of Principal Component Analysis (Linear), which can reduce the variables of mixed scale multivariable data (nominal, ordinal, interval, and ratio) simultaneously. This study investigated variance the estimation eigen value of Principal Component Analysis Linear and Nonlinear. The result showed that variance the estimation eigen value of Principal Component Analysis is
Var(
λ
^
˜
S
)=
H
S
′
V
S
H
S
$ {\rm Var}({\tilde{\hat{\lambda}}}_{S})=\mathbf H_{S}^{\prime}\mathbf V_{S}\mathbf H_{S} $
and variance the estimation eigen value of Nonlinear Principal Component Analysis is
Var(
λ
^
R
)=
H
R
′
V
R
H
R
$ {\rm Var}({{\hat{\lambda}}}_{R})=\mathbf H_{R}^{\prime}\mathbf V_{R}\mathbf H_{R} $
Variance the estimation eigen value of Nonlinear Principal Component Analysis better (efficient) than variance the estimation eigen value of Principal Component Analysis. |
| first_indexed | 2024-03-08T10:50:10Z |
| format | Article |
| id | doaj.art-3d032ca850ba439a965c505e877eefdc |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-08T10:50:10Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-3d032ca850ba439a965c505e877eefdc2024-01-26T16:47:48ZengEDP SciencesITM Web of Conferences2271-20972024-01-01580400110.1051/itmconf/20245804001itmconf_iicma2024_04001Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component AnalysisMakkulau0Tenri Ampa Andi1Yahya Irma2La Ome Lilis3Saidi La Ode4Faculty of Mathematics and Natural Sciences, Universitas of Halu OleoFaculty of Mathematics and Natural Sciences, Universitas of Halu OleoFaculty of Mathematics and Natural Sciences, Universitas of Halu OleoFaculty of Mathematics and Natural Sciences, Universitas of Halu OleoFaculty of Mathematics and Natural Sciences, Universitas of Halu Oleo, Computer Science DepartmentNonlinear Principal Component Analysis (PRINCALS) is an extension of Principal Component Analysis (Linear), which can reduce the variables of mixed scale multivariable data (nominal, ordinal, interval, and ratio) simultaneously. This study investigated variance the estimation eigen value of Principal Component Analysis Linear and Nonlinear. The result showed that variance the estimation eigen value of Principal Component Analysis is Var( λ ^ ˜ S )= H S ′ V S H S $ {\rm Var}({\tilde{\hat{\lambda}}}_{S})=\mathbf H_{S}^{\prime}\mathbf V_{S}\mathbf H_{S} $ and variance the estimation eigen value of Nonlinear Principal Component Analysis is Var( λ ^ R )= H R ′ V R H R $ {\rm Var}({{\hat{\lambda}}}_{R})=\mathbf H_{R}^{\prime}\mathbf V_{R}\mathbf H_{R} $ Variance the estimation eigen value of Nonlinear Principal Component Analysis better (efficient) than variance the estimation eigen value of Principal Component Analysis.https://www.itm-conferences.org/articles/itmconf/pdf/2024/01/itmconf_iicma2024_04001.pdf |
| spellingShingle | Makkulau Tenri Ampa Andi Yahya Irma La Ome Lilis Saidi La Ode Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis ITM Web of Conferences |
| title | Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis |
| title_full | Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis |
| title_fullStr | Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis |
| title_full_unstemmed | Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis |
| title_short | Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis |
| title_sort | variance the estimation eigen value of principal component analysis and nonlinear principal component analysis |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/01/itmconf_iicma2024_04001.pdf |
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