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Fisher regularized discriminative broad learning system for visual classification
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AI
DOI：10.1016/j.asoc.2024.112341.png)
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摘要
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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 l(2)-norm-based Broad Learning System (L2DBLS) introduces the epsilon-dragging technique to enhance labeling diversity, but the randomness inherent in epsilon-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.
Keyword:
Broad learning system
Fisher regularized
Label dragging
Visual classification
Regression labels
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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