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Machine Learning Approaches

In: Dimensionality Reduction in Data Science

Author

Listed:
  • Deepak Venugopal

    (The University of Memphis, Computer Science)

  • Max Garzon

    (The University of Memphis, Computer Science)

Abstract

Machine learning algorithms can train a model to extract some hidden patterns in a dataset to solve a problem or elucidate dependencies among the predictors and thus select or extract features that enable solutions to complex questions from large datasets. This chapter reviews various machine learning methods for dimensionality reduction, including autoencoders, neural networks themselves, and other methods.

Suggested Citation

  • Deepak Venugopal & Max Garzon, 2022. "Machine 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 179-197, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-05371-9_9
    DOI: 10.1007/978-3-031-05371-9_9
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