Efficient Decremental Learning Algorithms for Broad Learning System

TL;DR

This paper introduces efficient decremental learning algorithms for broad learning systems that prune redundant nodes and remove obsolete training samples, improving adaptability and computational efficiency.

Contribution

It develops novel decremental algorithms based on inverse matrix updates, extending previous incremental learning methods for broad learning systems.

Findings

Algorithms effectively prune redundant nodes and samples.

Recursive updates improve computational efficiency.

Enhanced adaptability of broad learning systems.

Abstract

The decremented learning algorithms are required in machine learning, to prune redundant nodes and remove obsolete inline training samples. In this paper, an efficient decremented learning algorithm to prune redundant nodes is deduced from the incremental learning algorithm 1 proposed in [9] for added nodes, and two decremented learning algorithms to remove training samples are deduced from the two incremental learning algorithms proposed in [10] for added inputs. The proposed decremented learning algorithm for reduced nodes utilizes the inverse Cholesterol factor of the Herminia matrix in the ridge inverse, to update the output weights recursively, as the incremental learning algorithm 1 for added nodes in [9], while that inverse Cholesterol factor is updated with an unitary transformation. The proposed decremented learning algorithm 1 for reduced inputs updates the output weights recursively with the inverse of the Herminia matrix in the ridge inverse, and updates that inverse recursively, as the incremental learning algorithm 1 for added inputs in [10].