A Hybrid Recursive Implementation of Broad Learning With Incremental Features

Journal article (2022)

Authors

Di Liu Southeast University
S. Baldi Team Bart De Schutter - Mechanical, Maritime and Materials Engineering , Southeast University
Wenwu Yu Southeast University
C. L.P. Chen South China University of Technology
Research Group
Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Di Liu, S. Baldi, Wenwu Yu, C. L.P. Chen
DOI: https://doi.org/10.1109/TNNLS.2020.3043110
Big data Training time Broad learning system (BLS) Recursive learning
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Bart De Schutter

Abstract

The broad learning system (BLS) paradigm has recently emerged as a computationally efficient approach to supervised learning. Its efficiency arises from a learning mechanism based on the method of least-squares. However, the need for storing and inverting large matrices can put the efficiency of such mechanism at risk in big-data scenarios. In this work, we propose a new implementation of BLS in which the need for storing and inverting large matrices is avoided. The distinguishing features of the designed learning mechanism are as follows: 1) the training process can balance between efficient usage of memory and required iterations (hybrid recursive learning) and 2) retraining is avoided when the network is expanded (incremental learning). It is shown that, while the proposed framework is equivalent to the standard BLS in terms of trained network weights,much larger networks than the standard BLS can be smoothly trained by the proposed solution, projecting BLS toward the big-data frontier.