TY - JOUR
T1 - Fisher regularized discriminative broad learning system for visual classification
AU - Li, Xianghua
AU - Wei, Jinlong
AU - Jin, Junwei
AU - Xu, Tao
AU - Yu, Dengxiu
N1 - Publisher Copyright:
© 2024
PY - 2024/12
Y1 - 2024/12
N2 - The Broad Learning System (BLS) is an innovative learning paradigm with significant success in image classification. However, the 0–1 labeling matrix employed in BLS struggles to align with the true data distribution, limiting the flexibility of the regression objective. The ℓ2-norm-based Broad Learning System (L2DBLS) introduces the ɛ-dragging technique to enhance labeling diversity, but the randomness inherent in ɛ-dragging weakens the label correlation within the same category. This paper proposes the Fisher Regularized Discriminative Broad Learning System (FRBLS) to tackle these issues and aims to achieve the following objectives: Firstly, the generated label matrix offers sufficient flexibility to maintain intra-class compactness and inter-class separability. Secondly, the constraints of Fisher Regularization on the same class of features ensure better alignment between samples and labels. Finally, the overall solution process is optimized using an ADAM-based alternating multiplier method, which ensures closed-form solutions at each iteration. Experimental results demonstrate that FRBLS achieves up to 98% accuracy on various face and object datasets, offering superior time efficiency and a 1% improvement in classification performance compared to recent state-of-the-art methods.
AB - The Broad Learning System (BLS) is an innovative learning paradigm with significant success in image classification. However, the 0–1 labeling matrix employed in BLS struggles to align with the true data distribution, limiting the flexibility of the regression objective. The ℓ2-norm-based Broad Learning System (L2DBLS) introduces the ɛ-dragging technique to enhance labeling diversity, but the randomness inherent in ɛ-dragging weakens the label correlation within the same category. This paper proposes the Fisher Regularized Discriminative Broad Learning System (FRBLS) to tackle these issues and aims to achieve the following objectives: Firstly, the generated label matrix offers sufficient flexibility to maintain intra-class compactness and inter-class separability. Secondly, the constraints of Fisher Regularization on the same class of features ensure better alignment between samples and labels. Finally, the overall solution process is optimized using an ADAM-based alternating multiplier method, which ensures closed-form solutions at each iteration. Experimental results demonstrate that FRBLS achieves up to 98% accuracy on various face and object datasets, offering superior time efficiency and a 1% improvement in classification performance compared to recent state-of-the-art methods.
KW - Broad learning system
KW - Fisher regularized
KW - Label dragging
KW - Regression labels
KW - Visual classification
UR - http://www.scopus.com/inward/record.url?scp=85207031887&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2024.112341
DO - 10.1016/j.asoc.2024.112341
M3 - 文章
AN - SCOPUS:85207031887
SN - 1568-4946
VL - 167
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 112341
ER -