<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Principal Component Analysis (PCA) is the main method of dimension reduction and data processing when the dataset is of high dimension. Therefore, PCA is a widely used method in almost all scientific fields. Because PCA is a linear combination of the original variables, the interpretation process of the analysis results is often encountered with some difficulties. The approaches proposed for solving these problems are called to as Sparse Principal Component Analysis (SPCA). Sparse approaches are not robust in existence of outliers in the data set. In this study, the performance of the approach proposed by Croux et al. (2013), which combines the advantageous properties of SPCA and Robust Principal Component Analysis (RPCA), will be examined through one real and three artificial datasets in the situation of full sparseness. In the light of the findings, it is recommended to use robust sparse PCA based on projection pursuit in analyzing the data. Another important finding obtained from the study is that the BIC and TPO criteria used in determining lambda are not much superior to each other. We suggest choosing one of these two criteria that give an optimal result.</p>
</abstract>

Principal Component Analysis (PCA) is the main method of dimension reduction and data processing when the dataset is of high dimension. Therefore, PCA is a widely used method in almost all scientific fields. Because PCA is a linear combination of the original variables, the interpretation process of the analysis results is often encountered with some difficulties. The approaches proposed for solving these problems are called to as Sparse Principal Component Analysis (SPCA). Sparse approaches are not robust in existence of outliers in the data set. In this study, the performance of the approach proposed by Croux et al. (2013), which combines the advantageous properties of SPCA and Robust Principal Component Analysis (RPCA), will be examined through one real and three artificial datasets in the situation of full sparseness. In the light of the findings, it is recommended to use robust sparse PCA based on projection pursuit in analyzing the data. Another important finding obtained from the study is that the BIC and TPO criteria used in determining lambda are not much superior to each other. We suggest choosing one of these two criteria that give an optimal result.

Robust sparse principal component analysis: situation of full sparseness

Department of Educational Measurement and Evaluation

Department of Department of Biostatistics and Medical Informatics

Journal of Applied Mathematics, Statistics and Informatics

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Robust sparse principal component analysis: situation of full sparseness"}

Principal Component Analysis (PCA) is the main method of dimension reduction and data processing when the dataset is of high dimension. Therefore, PCA is a...

Robust sparse principal component analysis: situation of full sparseness

Data publikacji: 04 lip 2022

Zakres stron: 5 - 20

DOI: https://doi.org/10.2478/jamsi-2022-0001

Słowa kluczowe
Principal Component, Outliers, Sparsity, Robustness

© 2022 B. Bariş Alkan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Robust sparse principal component analysis: situation of full sparseness

Data publikacji: 04 lip 2022

Zakres stron: 5 - 20

DOI: https://doi.org/10.2478/jamsi-2022-0001

Słowa kluczowePrincipal Component, Outliers, Sparsity, Robustness

© 2022 B. Bariş Alkan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Principal Component, Outliers, Sparsity, Robustness