Incremental Semisupervised Learning With Adaptive Locality Preservation for High-Dimensional Data

Broad Learning System (BLS) has been widely researched and applied in the field of semi-supervised learning. However, current semi-supervised BLS methods rely on pre-defined graph structures. High-dimensional small-sample data, characterized by abundant redundant and noisy features with complex distribution patterns, often leads to the construction of poor-quality pre-defined graphs, thereby constraining the model’s performance. Additionally, the random generation of feature and enhancement nodes in BLS, combined with limited data labels, results in suboptimal model performance. To address these issues, this paper first proposes a Broad Learning System with Adaptive Locality Preservation (BLS-ALP). This method employs adaptive locality preservation constraints in the output space to ensure that similar samples share the same label, iteratively updating the graph structure. To further enhance the performance of BLS-ALP, an incremental ensemble framework (IBLS-ALP) is proposed. This framework effectively mitigates the impact of redundant and noisy features by using multiple random subspaces instead of the original high-dimensional space. Additionally, IBLS-ALP enhances the utilization of a small number of labels by incorporating residual labels, thereby significantly improving the model’s overall performance. Extensive experiments conducted on various high-dimensional small-sample datasets demonstrate that IBLS-ALP exhibits superior performance.