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Solutions to Data Science Problems

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Dimensionality Reduction in Data Science

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Abstract

This chapter presents a review of statistical and machine learning models to tackle data science problems, arguably the most popular approaches. Both supervised and unsupervised algorithms are described along with practical considerations when using these methods. Empirical results on exemplar datasets are also presented where applicable to illustrate the application of these methods to real-world problems.

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Author information

Authors and Affiliations

  1. Computer Science, The University of Memphis, Memphis, TN, USA

    Deepak Venugopal & Max Garzon

  2. Mathematical Sciences, The University of Memphis, Memphis, TN, USA

    Lih-Yuan Deng

Authors
  1. Deepak Venugopal
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  2. Lih-Yuan Deng
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  3. Max Garzon
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Corresponding author

Correspondence to Deepak Venugopal .

Editor information

Editors and Affiliations

  1. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Max Garzon

  2. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Ching-Chi Yang

  3. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Deepak Venugopal

  4. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Nirman Kumar

  5. Memphis, TN, USA

    Kalidas Jana

  6. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Lih-Yuan Deng

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Venugopal, D., Deng, LY., Garzon, M. (2022). Solutions to Data Science Problems. In: Garzon, M., Yang, CC., Venugopal, D., Kumar, N., Jana, K., Deng, LY. (eds) Dimensionality Reduction in Data Science. Springer, Cham. https://doi.org/10.1007/978-3-031-05371-9_2

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