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Statistical Learning Approaches

In: Dimensionality Reduction in Data Science

Author

Listed:
  • Ching-Chi Yang

    (The University of Memphis, Mathematical Sciences)

  • Lih-Yuan Deng

    (The University of Memphis, Mathematical Sciences)

Abstract

Instead of retaining certain properties when selecting or extracting features, other methods aim to remove irrelevant and/or redundant features in the data using primarily statistical criteria. Features are now selected or extracted that have the highest impact on the prediction of the response/target variable based on various statistical solution methods. This chapter describes methods using linear regression and regularization that afford solutions to dimensionality reduction and solutions to problems that are explainable to humans.

Suggested Citation

  • Ching-Chi Yang & Lih-Yuan Deng, 2022. "Statistical Learning Approaches," 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 169-177, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-05371-9_8
    DOI: 10.1007/978-3-031-05371-9_8
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