Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction
Joint Authors
Zhang, Zhaoyang
Sun, Shijie
Wang, Wei
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-29
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Zhaoyang& Sun, Shijie& Wang, Wei. 2020. Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1144996
Modern Language Association (MLA)
Zhang, Zhaoyang…[et al.]. Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1144996
American Medical Association (AMA)
Zhang, Zhaoyang& Sun, Shijie& Wang, Wei. Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1144996
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144996