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On the Correlation between Random Variables and their Principal Components

arXiv 2310.06139 ↗First posted 9 Oct 2023cs.LG math.ST stat.THcited by 0

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Abstract (arXiv)

The article attempts to find an algebraic formula describing the correlation coefficients between random variables and the principal components representing them. As a result of the analysis, starting from selected statistics relating to individual random variables, the equivalents of these statistics relating to a set of random variables were presented in the language of linear algebra, using the concepts of vector and matrix. This made it possible, in subsequent steps, to derive the expected formula. The formula found is identical to the formula used in Factor Analysis to calculate factor loadings. The discussion showed that it is possible to apply this formula to optimize the number of principal components in Principal Component Analysis, as well as to optimize the number of factors in Factor Analysis.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 14 references that are in the map. First posted on arXiv 9 Oct 2023.

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References · 2

  1. 2017New Interpretation of Principal Components Analysiscited by 2
  2. 2023On the Analysis of Correlation Between Nominal Data and Numerical Datacited by 2

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