Conventional Statistical Approaches

Ching-Chi Yang (), Max Garzon () and Lih-Yuan Deng ()
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Ching-Chi Yang: The University of Memphis, Mathematical Sciences
Max Garzon: The University of Memphis, Computer Science
Lih-Yuan Deng: The University of Memphis, Mathematical Sciences

Chapter Chapter 4 in Dimensionality Reduction in Data Science, 2022, pp 79-95 from Springer

Abstract: Abstract The objective of dimensionality reduction is to retain key properties of the given data to solve a problem with fewer features in a lower dimensional space. Statistical methods aim to preserve characteristic parameters such as mean, variance, and covariance of features in the population, as estimated from the dataset. Methods include Principal Component Analysis (PCA) and its variants, Independent component analysis and Discriminant Analysis. Linear algebra methods offer other approaches, including Singular value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF).

Date: 2022
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DOI: 10.1007/978-3-031-05371-9_4

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