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  3. Multi-source Learning Via Completion Of Block-wise Overlapping Noisy Matrices.
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  3. Multi-source Learning Via Completion Of Block-wise Overlapping Noisy Matrices.

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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
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Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.

Doudou Zhou1, Tianxi Cai1, Junwei Lu1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.

Journal of Machine Learning Research : JMLR
|March 30, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new Block-wise Overlapping Noisy Matrix Integration (BONMI) algorithm effectively integrates multi-source electronic healthcare records (EHR) data. This method improves feature representation and enables cross-lingual medical concept tasks, outperforming existing approaches.

Keywords:
Word embeddingdata integrationsingular value decompositiontransfer learning

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Area of Science:

  • Computational biology
  • Data science
  • Medical informatics

Background:

  • Electronic healthcare records (EHR) offer valuable data for research.
  • Representing EHR features from diverse sources, including unstructured narratives and structured data, is challenging.
  • Existing matrix factorization methods struggle with multi-source data containing overlapping yet non-identical features.

Purpose of the Study:

  • To propose a novel word embedding generative model for multi-source EHR data.
  • To develop an efficient algorithm for optimal aggregation of multi-source pointwise mutual information matrices.
  • To address limitations in current matrix completion techniques by considering non-independent missing data mechanisms.

Main Methods:

  • Proposed a novel word embedding generative model for multi-source EHR data.
  • Designed an efficient Block-wise Overlapping Noisy Matrix Integration (BONMI) algorithm.
  • Analyzed the theoretical guarantees and statistical recovery rates of the proposed estimator for matrix completion.

    • Main Results:

    • BONMI optimally aggregates multi-source pointwise mutual information matrices with theoretical guarantees.
    • The proposed method demonstrates effectiveness in matrix completion without assuming independent missingness.
    • Simulation studies confirm BONMI's robust performance across various configurations.

    Conclusions:

    • BONMI successfully integrates multi-lingual, multi-source medical text and EHR data.
    • The method enables effective co-training of semantic embeddings and translation of medical concepts between English and Chinese.
    • BONMI offers advantages over existing methods for multi-source EHR data representation and integration.