Cauchy regularized broad learning system for noisy data regression

Liu, Licheng; Cai, Luyang; Liu, Tingyun; Chen, C.L. Philip; Xiao-qin, Tang

doi:10.1016/j.ins.2022.04.051

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…Xu Lili proposed a Bayesian broad learning system based on graph regularization to improve the stability and generalization ability of BLS [19]. Licheng Liu proposed a regularized broad learning system for noisy data regression that can handle data contaminated and surrounded by noise [20]. Research on BLS has also ventured into time synchronization [21], clinical healthcare [22][23][24], complex industrial process control [25,26] and optimization, computer vision [27], and more.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving vertical federated broad learning system for artificial intelligence generated image content

Li,

Ge,

Wang

et al. 2024

J Real-Time Image Proc

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Artificial intelligence generated image content generates desired image content according to given instructions. It is widely applied in digital marketing, video game design, and movie production. Efficiently detecting the quality of generated images is one of the key problems. A broad learning system is a feasible solution because it requires no complex parameter calculation and depth expansion. However, the broad learning system is built by centralized learning, so it may not take advantage of isolated feature data, which prevents broad learning systems from detecting isolated AL-generated image content. To address the above problem, this paper proposes a privacy-preserving vertical federated broad learning system for artificial intelligence generated image content (PVF-BLS). In PVF-BLS, the vertical federated architecture is introduced into the broad learning system to utilize isolated feature data and interactive data is masked by matrix masks for security. To further improve the security of PVF-BLS, this paper proposes a secure incremental learning algorithm based on matrix masks (ILA-MM) to update PVF-BLS. In ILA-MM, the interaction data is processed by matrix masks, which makes PVF-BLS more secure. Experimental results show that PVF-BLS outperforms traditional broad learning systems when the data is isolated.

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Section: Introductionmentioning

confidence: 99%

Privacy-preserving vertical federated broad learning system for artificial intelligence generated image content

Li,

Ge,

Wang

et al. 2024

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…Then, Zheng et al designed a broad learning system based on maximum correntropy criterion (BLS-MCC) which used maximum correntropy criterion to calculate weights of training samples [24]. In addition, Liu et al adopted Cauchy loss function to process the noisy data [25]. Meanwhile, ℓ 1 norm cost function and ℓ 2 regularization method were used in robust broad learning system (RBLS) [26], then elastic-net regularization approach replaced ℓ 2 regularization method in RBLS [27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Stacked Broad Learning System for Noisy Data Regression

Zheng,

Liu

2024

IJACSA

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Robust broad learning system (RBLS) demonstrates the generalization and robustness for solving uncertain data regression tasks. To enhance representation ability of RBLS, this paper aims at developing a novel robust stacked broad learning system for solving noisy data regression problems, termed as RSBLS. In our work, we expand traditional BLS into a stacked broad learning system model with deep structure of feature nodes and enhancement nodes. Furthermore, ℓ 1 norm loss function is employed to update the objective function of RSBLS for processing noisy data, we apply augmented Lagrange multiplier (ALM) to get the output weights of RSBLS which keeps the effectiveness and efficiency compared with weighted loss function. Simulation results over some regression datasets with outliers demonstrate that, the proposed RSBLS performs favorably with better robustness with respect to RVFL, BLS, Huber-WBLS, KDE-WBLS and RBLS.

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“…Jin [13] proposed a novel discriminative group sparse broad learning system to enhance the classification ability of BLS in visual recognition. Liu [14] proposed a regularized broad learning system to address outliers and Gaussian noise in data modeling BLS can expand the network horizontally to meet different task requirements, which raises the problem of how far the network needs to expand. On the one hand, the fewer nodes, the narrower the network, and the simpler the model, which may lead to poor performance of the model.…”

Section: Introductionmentioning

confidence: 99%

Pruned Broad Learning System Based on Sparse Ridge Fusion

Chu,

Lu,

et al. 2023

Preprint

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Broad learning system is an emerging method, which has achieved outstanding performance in regression and classification problems. This paper proposes a novel algorithm called Pruned Broad Learning System (PBLS) to reduce model size and improve model interpretability. The proposed PBLS introduces the sparse ridge fusion penalty into BLS, which combines the \({L_1} - norm\) regularizer and second order difference penalty together. The \({L_1} - norm\) regularizer is used to sparse the model by making insignificant output weights toward zero, while second order difference penalty is used to smooth output weights by penalizing the roughness of the model. Then, the unimportant nodes are pruned from the model. The remaining useful nodes are divided in the form of block structure, and their output weights change slowly and smoothly in their respective block structure. The experiments on several commonly used regression data sets are carried out to verify the feasibility of the proposed pruned BLS. The experiment results indicate that pruned BLS can reduce model complexity without loss of prediction accuracy and improve the model interpretability.

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Cauchy regularized broad learning system for noisy data regression

Cited by 12 publications

References 32 publications

Privacy-preserving vertical federated broad learning system for artificial intelligence generated image content

Privacy-preserving vertical federated broad learning system for artificial intelligence generated image content

A Novel Robust Stacked Broad Learning System for Noisy Data Regression

Pruned Broad Learning System Based on Sparse Ridge Fusion

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