TY - GEN
T1 - Multilayer Subspace Learning With Self-Sparse Robustness for Two-Dimensional Feature Extraction
AU - Zhang, Han
AU - Gong, Maoguo
AU - Nie, Feiping
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Two-dimensional (2D) feature extraction techniques are specifically designed for reducing the dimension of data in matrix representation. Existing methods mostly rely on bilateral projections of matrices. This rasterized manner critically limits the freedom of feature combinations, and thus degrades the fitting ability of models. The robustness of previous techniques is also unsatisfactory due to the insufficiency of discerning outliers with severe damages. In this work, we propose a novel bilinear subspace learning model to achieve flexible and robust two-dimensional feature extraction. The features are exploited in a multilayer bilinear low-rank space under a collaborative orthogonality constraint, and a self-sparse robust strategy is imposed. The model greatly extends the projection space and relaxes the restriction in conventional orthogonal space. We accordingly design a tactful and efficient optimization method based on the coordinate descent method, optimally addressing the proposed model. Experimental results demonstrate the excited improvements of our extracted features in image classification task.
AB - Two-dimensional (2D) feature extraction techniques are specifically designed for reducing the dimension of data in matrix representation. Existing methods mostly rely on bilateral projections of matrices. This rasterized manner critically limits the freedom of feature combinations, and thus degrades the fitting ability of models. The robustness of previous techniques is also unsatisfactory due to the insufficiency of discerning outliers with severe damages. In this work, we propose a novel bilinear subspace learning model to achieve flexible and robust two-dimensional feature extraction. The features are exploited in a multilayer bilinear low-rank space under a collaborative orthogonality constraint, and a self-sparse robust strategy is imposed. The model greatly extends the projection space and relaxes the restriction in conventional orthogonal space. We accordingly design a tactful and efficient optimization method based on the coordinate descent method, optimally addressing the proposed model. Experimental results demonstrate the excited improvements of our extracted features in image classification task.
KW - multilayer bilinear subspace learning
KW - Two-dimensional feature extraction
UR - http://www.scopus.com/inward/record.url?scp=85177552141&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10094578
DO - 10.1109/ICASSP49357.2023.10094578
M3 - 会议稿件
AN - SCOPUS:85177552141
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -