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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Bidimensionally partitioned online sequential broad learning system for large-scale data stream modeling.

Wei Guo1,2, Jianjiang Yu3, Caigen Zhou2

  • 1Jiangsu Provincial University Key Lab of Child Cognitive Development and Mental Health, Yancheng Teachers University, Yancheng, 224002, China.

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Incremental broad learning system (IBLS) faces challenges with large datasets due to high computational and storage demands. A new bidimensionally partitioned online sequential broad learning system (BPOSBLS) addresses this by decomposing problems and using recursive methods for efficient, lightweight learning.

Keywords:
Big data modelingBroad learning systemMatrix partitioningOnline sequential learningPartitioned recursive least squares

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Incremental broad learning system (IBLS) offers efficient incremental learning for streaming data.
  • IBLS faces limitations in large-scale scenarios due to retaining all historical data and requiring large network sizes for accuracy.
  • These limitations increase computational overhead and storage burden, hindering scalability.

Purpose of the Study:

  • To propose a novel Bidimensionally Partitioned Online Sequential Broad Learning System (BPOSBLS).
  • To address the scalability and efficiency issues of IBLS in large-scale data streams.
  • To develop a lightweight online sequential learning algorithm with reduced computational costs and storage requirements.

Main Methods:

  • BPOSBLS partitions the high-dimensional broad feature matrix bidimensionally (instance and feature dimensions).
  • Decomposes large least squares problems into smaller, individually solvable ones.
  • Employs a partitioned recursive least squares method for iterative updating using only current online samples.

Main Results:

  • Substantially diminishes the scale and computational complexity of the original high-order model.
  • Significantly improves learning efficiency and usability for large-scale complex learning tasks.
  • Demonstrates consistently low computational costs and storage requirements, making it a lightweight algorithm.

Conclusions:

  • BPOSBLS effectively overcomes the limitations of traditional IBLS for large-scale data streams.
  • The proposed algorithm offers superior efficiency and scalability.
  • Theoretical analyses and simulations confirm the effectiveness and advantages of BPOSBLS.