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Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data.

Satish Viswanath1, Anant Madabhushi

  • 1Dept. of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, New Jersey 08854, USA. satish@eden.rutgers.edu

BMC Bioinformatics
|February 10, 2012
PubMed
Summary

Consensus embedding is a novel dimensionality reduction technique that overcomes noise and parameter sensitivity by combining multiple embeddings. This method offers a stable, information-preserving solution outperforming existing approaches in biomedical data analysis.

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Area of Science:

  • Biomedical Data Analysis
  • Machine Learning
  • Computational Biology

Background:

  • Dimensionality reduction (DR) techniques are crucial for creating lower-dimensional embeddings from high-dimensional data while preserving class discriminability.
  • Popular DR methods often exhibit sensitivity to parameter choices and data noise.
  • Consensus embedding is introduced as a novel DR technique to address these limitations.

Purpose of the Study:

  • To present a novel dimensionality reduction technique called consensus embedding.
  • To demonstrate its theoretical properties for stable and accurate information preservation.
  • To provide an efficient implementation using intelligent sub-sampling and parallelization.

Main Methods:

  • Consensus embedding generates and combines multiple low-dimensional embeddings, similar to ensemble learning.

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  • It exploits variance among embeddings to achieve a stable solution.
  • Intelligent sub-sampling (mean-shift) and code parallelization ensure efficient implementation.

    • Main Results:

    • Consensus embedding was applied to diverse biomedical datasets, including brain MRI, gene expression, and prostate MRI.
    • It consistently outperformed linear and non-linear DR methods in classification and clustering tasks across over 200 experiments.
    • The method demonstrated effectiveness in image partitioning, gene expression classification, and cancer detection.

    Conclusions:

    • Consensus embedding offers a novel framework leveraging ensemble theory for DR in high-dimensional biomedical data.
    • It improves data representation and classification for both imaging and non-imaging data.
    • The generalizable framework provides a promising solution for DR challenges and potential extension to other biomedical data analysis areas.