A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object Recognition Article Swipe
Xiaoxue Li
,
Weijia Feng
,
Xiaofeng Wang
,
Jia Guo
,
Yuanxu Chen
,
Yumeng Yang
,
Chao Wang
,
Xinyu Zuo
,
Minming Xu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12183745
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12183745
A principal component analysis network (PCANet), as one of the representative deep subspace learning networks, utilizes principal component analysis (PCA) to learn filters that represent the dominant structural features of objects. However, the filters used in PCANet are linear combinations of all the original variables and contain complex and redundant principal components, which hinders the interpretability of the results. To address this problem, we introduce sparse constraints into a subspace learning network and propose three sparse bi-directional two-dimensional PCANet algorithms, including sparse row 2D2PCANet (SR2D2PCANet), sparse column 2D2PCANet (SC2D2PCANet), and sparse row–column 2D2PCANet (SRC2D2PCANet). These algorithms perform sparse operations on the projection matrices in the row, column, and row–column direction, respectively. Sparsity is achieved by utilizing the elastic net to shrink the loads of the non-primary elements in the principal components to zero and to reduce the redundancy in the projection matrices, thus improving the learning efficiency of the networks. Finally, a variety of experimental results on ORL, COIL-100, NEC, and AR datasets demonstrate that the proposed algorithms learn filters with more discriminative information and outperform other subspace learning networks and traditional deep learning networks in terms of classification and run-time performance, especially for less sample learning.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12183745
- https://www.mdpi.com/2079-9292/12/18/3745/pdf?version=1693962719
- OA Status
- gold
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386461644
All OpenAlex metadata
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- OpenAlex ID
-
https://openalex.org/W4386461644Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics12183745Digital Object Identifier
- Title
-
A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-05Full publication date if available
- Authors
-
Xiaoxue Li, Weijia Feng, Xiaofeng Wang, Jia Guo, Yuanxu Chen, Yumeng Yang, Chao Wang, Xinyu Zuo, Minming XuList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12183745Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/12/18/3745/pdf?version=1693962719Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/12/18/3745/pdf?version=1693962719Direct OA link when available
- Concepts
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Subspace topology, Principal component analysis, Sparse approximation, Discriminative model, Artificial intelligence, Redundancy (engineering), Computer science, Pattern recognition (psychology), Interpretability, Projection (relational algebra), Sparse PCA, Feature learning, Algorithm, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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