Broad Learning System with Coordinate Iterative Training Mode

Broad learning system (BLS) has emerged as an effective alternative to deep learning. Due to the linear output characteristics of BLS, the generalized inverse method based on ridge regression is commonly used to solve for the hidden layer weights. However, when dealing with large matri-ces, the generalized inverse algorithm often underperforms. Moreover, it considers all features simultaneously during computation, resulting in redundancy and inefficiency. To address these issues, we first extend the RCD algorithm for multi-output problems, enabling parallel computation of multiple systems. Furthermore, the introduction of a novel iterative update strategy allows for the incorporation of randomness while capturing important low-probability feature information, thereby achieving superior results. Thus, we propose an enhanced parallel randomized coordinate de-scent (EPRCD) algorithm for weight calculation. Numerical experiments demonstrate that the newly proposed algorithm performs well on hlah-dimcnsional datasets.