Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction
The matrix-based features can provide valid and interpretable information for matrix-based data such as image. Matrix-based kernel principal component analysis (MKPCA) is a way for extracting matrix-based features. The extracted matrix-based feature is useful to both dimension reduction and spatial...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8864594 |
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| _version_ | 1832554587108671488 |
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| author | Zhaoyang Zhang Shijie Sun Wei Wang |
| author_facet | Zhaoyang Zhang Shijie Sun Wei Wang |
| author_sort | Zhaoyang Zhang |
| collection | DOAJ |
| description | The matrix-based features can provide valid and interpretable information for matrix-based data such as image. Matrix-based kernel principal component analysis (MKPCA) is a way for extracting matrix-based features. The extracted matrix-based feature is useful to both dimension reduction and spatial statistics analysis for an image. In contrast, the efficiency of MKPCA is highly restricted by the dimension of the given matrix data and the size of the training set. In this paper, an incremental method to extract features of a matrix-based dataset is proposed. The method is methodologically consistent with MKPCA and can improve efficiency through incrementally selecting the proper projection matrix of the MKPCA by rotating the current subspace. The performance of the proposed method is evaluated by performing several experiments on both point and image datasets. |
| format | Article |
| id | doaj-art-aa76374b1f5f497fbe77e03810734765 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-aa76374b1f5f497fbe77e038107347652025-02-03T05:51:12ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88645948864594Incremental Matrix-Based Subspace Method for Matrix-Based Feature ExtractionZhaoyang Zhang0Shijie Sun1Wei Wang2School of Information Engineering, Chang’an University, Xi’an 710068, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710068, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710068, ChinaThe matrix-based features can provide valid and interpretable information for matrix-based data such as image. Matrix-based kernel principal component analysis (MKPCA) is a way for extracting matrix-based features. The extracted matrix-based feature is useful to both dimension reduction and spatial statistics analysis for an image. In contrast, the efficiency of MKPCA is highly restricted by the dimension of the given matrix data and the size of the training set. In this paper, an incremental method to extract features of a matrix-based dataset is proposed. The method is methodologically consistent with MKPCA and can improve efficiency through incrementally selecting the proper projection matrix of the MKPCA by rotating the current subspace. The performance of the proposed method is evaluated by performing several experiments on both point and image datasets.http://dx.doi.org/10.1155/2020/8864594 |
| spellingShingle | Zhaoyang Zhang Shijie Sun Wei Wang Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction Complexity |
| title | Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction |
| title_full | Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction |
| title_fullStr | Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction |
| title_full_unstemmed | Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction |
| title_short | Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction |
| title_sort | incremental matrix based subspace method for matrix based feature extraction |
| url | http://dx.doi.org/10.1155/2020/8864594 |
| work_keys_str_mv | AT zhaoyangzhang incrementalmatrixbasedsubspacemethodformatrixbasedfeatureextraction AT shijiesun incrementalmatrixbasedsubspacemethodformatrixbasedfeatureextraction AT weiwang incrementalmatrixbasedsubspacemethodformatrixbasedfeatureextraction |