Most of the existing classification systems assume that the data used is high-quality labeled. However, the labeling process in real-world may inevitably introduce corruptions into labels which can confuse the performances of classifiers. In this paper, based on Broad Learning System (BLS), we propose a novel label noise tolerant method to classify the pattern with corrupted labels. The standard BLS has shown promising efficiency and accuracy in general classification, but its learning process is prone to be affected by the noisy labels. Here, by detailed probabilistic analysis, we first give the reason for lacks of robustness in standard BLS. Then a maximum likelihood estimation-based objective function is derived for robust classification. In addition, a manifold regularization term is integrated to preserve the local geometry of data, which makes the model to be more robust and flexible to learn the output weights. Given some basic assumptions on the approximation errors, the obtained model can be transformed to a graph regularized reweighted BLS problem. The negative effects of noisy labels in data can be inhibited adaptively by assigning reasonable weights. Theoretical analysis and extensive experiments are provided to demonstrate the robustness and effectiveness of the proposed robust BLS model, especially for the case of large amounts of noisy labels.

Broad learning system (BLS), which emerges as a lightweight network paradigm, has recently attracted great attention for recognition problems due to its good balance between efficiency and accuracy. However, the supervision mechanism in BLS and its variants generally relies on the strict binary label matrix, which imposes limitations on approximation and fails to adequately align with the data distribution. To address this issue, in this article, two novel flexible label-induced BLS models with the manifold manner are proposed, whose notable characteristics are as follows. First, two proposed label relaxation strategies can both enlarge the margins between different categories and simultaneously enhance the diversity within labels. Second, the integration of manifold geometrical criterion enables the models to capture local feature structures, ensuring the obtained flexible labels align better with the similarity between samples. Third, the proposed models can be optimized efficiently with the alternating direction method of multipliers. Each iteration benefits from a closed-form solution, facilitating the optimization process. Extensive experiments and thorough theoretical analysis are intended to show the advantages of our proposed models compared to other state-of-the-art recognition algorithms.

For multimodal medical image fusion problems, most of the existing fusion approaches are based on pixel-level. However, the pixel-based fusion method tends to lose local and spatial information as the relationships between pixels are not considered appropriately, which has much influence on the quality of the fusion results. To address this issue, a region-based multimodal medical image fusion framework is proposed based on superpixel segmentation and a post-processing optimization method in this paper. In this framework, the average image of the source medical images is firstly obtained by a weighted averaging method. To effectively obtain homogeneous regions and preserve the complete information of image details, the fast linear spectral clustering(LSC) superpixel algorithm is carried out to segment the average image and get superpixel labels. For each region of the medical images, log-gabor filter(LGF) and sum modified laplacian(SML) are adopted to extract texture feature and contrast feature for the measurement of region importance. The most important regions are selected and the decision map is generated by comparison. Moreover, to get a more accurate decision map, a new post-processing optimized method based on genetic algorithm(GA) is given. A weighted strategy is applied to the extracted features and the weighting factor can be adaptively adjusted by GA. The effectiveness of the proposed fusion method is validated by conducting experiments on eight pairs of medical images from diverse modalities. In addition, seven other mainstream medical image fusion methods are adopted for comparing the performance of fusion. Experimental results in terms of qualitative and quantitative evaluation demonstrate that the proposed method can achieve state-of-the-art performance for multimodal medical image fusion problems.

The broad learning system (BLS) is a lightweight neural network known for its efficient learning capabilities; however, it is limited by its reliance on a binary label strategy. Existing label enhancement models primarily focus on increasing the distances between labels from different classes, which inadvertently expands the distance within the same category. For classification tasks, maintaining similarity within the intraclass is essential for ensuring the model’s effectiveness. To address this issue, we propose a groupwise label enhancement BLS model that ensures both intraclass similarity and interclass disparity of labels. Specifically, we develop a novel regression target that generalizes existing label enhancement targets in BLS, increasing the distances between labels of different classes while overcoming the constraints imposed by binary labels. Moreover, we design a groupwise constraint to jointly enhance the intraclass similarity and interclass disparity of labels. Additionally, we propose a novel alternating direction method of multipliers-based optimization algorithm to solve our proposed model, ensuring both computational efficiency and theoretical convergence. Experimental results on several public datasets demonstrate the outstanding effectiveness and efficiency of our proposed model compared to other state-of-the-art methods.