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Supervised dimensionality reduction for big data

Author

Listed:
  • Joshua T. Vogelstein

    (Johns Hopkins University)

  • Eric W. Bridgeford

    (Johns Hopkins University)

  • Minh Tang

    (Johns Hopkins University)

  • Da Zheng

    (Johns Hopkins University)

  • Christopher Douville

    (Johns Hopkins University)

  • Randal Burns

    (Johns Hopkins University)

  • Mauro Maggioni

    (Johns Hopkins University)

Abstract

To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

Suggested Citation

  • Joshua T. Vogelstein & Eric W. Bridgeford & Minh Tang & Da Zheng & Christopher Douville & Randal Burns & Mauro Maggioni, 2021. "Supervised dimensionality reduction for big data," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23102-2
    DOI: 10.1038/s41467-021-23102-2
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    Cited by:

    1. Gemma Turon & Jason Hlozek & John G. Woodland & Ankur Kumar & Kelly Chibale & Miquel Duran-Frigola, 2023. "First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Akampurira Paul & Mutebi Joe & Mugisha Brian & Muhaise Hussein & Kyomuhangi Rosette, 2024. "Exploring Dimensionality Reduction Techniques for Improved Breast Cancer Diagnosis," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(5), pages 808-824, May.

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