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What Is Dimensionality Reduction (DR)?

In: Dimensionality Reduction in Data Science

Author

Listed:
  • Lih-Yuan Deng

    (The University of Memphis, Mathematical Sciences)

  • Max Garzon

    (The University of Memphis, Computer Science)

  • Nirman Kumar

    (The University of Memphis, Computer Science)

Abstract

Solutions to problems require either assumptions on the target population or lots of data to train models that may help answer the questions. Our ability to generate, gather, and store volumes of data (order of tera- and exo-bytes, 1012 − 1018 daily) has far outpaced our ability to derive useful information from it in many fields, with available computational resources. Therefore, data reduction is a critical step in order to turn large datasets into useful information, the overarching purpose of data science. DR thus becomes absolutely essential in DS, particularly for big data.

Suggested Citation

  • Lih-Yuan Deng & Max Garzon & Nirman Kumar, 2022. "What Is Dimensionality Reduction (DR)?," Springer Books, in: Max Garzon & Ching-Chi Yang & Deepak Venugopal & Nirman Kumar & Kalidas Jana & Lih-Yuan Deng (ed.), Dimensionality Reduction in Data Science, chapter 0, pages 67-77, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-05371-9_3
    DOI: 10.1007/978-3-031-05371-9_3
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