A subspace type incremental two-dimensional principal component analysis algorithm
Principal component analysis (PCA) has been a powerful tool for high-dimensional data analysis. It is usually redesigned to the incremental PCA algorithm for processing streaming data. In this paper, we propose a subspace type incremental two-dimensional PCA algorithm (SI2DPCA) derived from an incre...
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doaj-0c718adc3c8342feac4c13a48c51c6ea2020-12-02T22:07:32ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30262020-11-011410.1177/1748302620973531A subspace type incremental two-dimensional principal component analysis algorithmXiaowei ZhangZhongming TengPrincipal component analysis (PCA) has been a powerful tool for high-dimensional data analysis. It is usually redesigned to the incremental PCA algorithm for processing streaming data. In this paper, we propose a subspace type incremental two-dimensional PCA algorithm (SI2DPCA) derived from an incremental updating of the eigenspace to compute several principal eigenvectors at the same time for the online feature extraction. The algorithm overcomes the problem that the approximate eigenvectors extracted from the traditional incremental two-dimensional PCA algorithm (I2DPCA) are not mutually orthogonal, and it presents more efficiently. In numerical experiments, we compare the proposed SI2DPCA with the traditional I2DPCA in terms of the accuracy of computed approximations, orthogonality errors, and execution time based on widely used datasets, such as FERET, Yale, ORL, and so on, to confirm the superiority of SI2DPCA.https://doi.org/10.1177/1748302620973531 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaowei Zhang Zhongming Teng |
spellingShingle |
Xiaowei Zhang Zhongming Teng A subspace type incremental two-dimensional principal component analysis algorithm Journal of Algorithms & Computational Technology |
author_facet |
Xiaowei Zhang Zhongming Teng |
author_sort |
Xiaowei Zhang |
title |
A subspace type incremental two-dimensional principal component analysis algorithm |
title_short |
A subspace type incremental two-dimensional principal component analysis algorithm |
title_full |
A subspace type incremental two-dimensional principal component analysis algorithm |
title_fullStr |
A subspace type incremental two-dimensional principal component analysis algorithm |
title_full_unstemmed |
A subspace type incremental two-dimensional principal component analysis algorithm |
title_sort |
subspace type incremental two-dimensional principal component analysis algorithm |
publisher |
SAGE Publishing |
series |
Journal of Algorithms & Computational Technology |
issn |
1748-3026 |
publishDate |
2020-11-01 |
description |
Principal component analysis (PCA) has been a powerful tool for high-dimensional data analysis. It is usually redesigned to the incremental PCA algorithm for processing streaming data. In this paper, we propose a subspace type incremental two-dimensional PCA algorithm (SI2DPCA) derived from an incremental updating of the eigenspace to compute several principal eigenvectors at the same time for the online feature extraction. The algorithm overcomes the problem that the approximate eigenvectors extracted from the traditional incremental two-dimensional PCA algorithm (I2DPCA) are not mutually orthogonal, and it presents more efficiently. In numerical experiments, we compare the proposed SI2DPCA with the traditional I2DPCA in terms of the accuracy of computed approximations, orthogonality errors, and execution time based on widely used datasets, such as FERET, Yale, ORL, and so on, to confirm the superiority of SI2DPCA. |
url |
https://doi.org/10.1177/1748302620973531 |
work_keys_str_mv |
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1724401812430127104 |