Covariance-based Principal Components Analysis (PCA) Algorithm

A Covariance-based Principal Components Analysis (PCA) Algorithm is a PCA algorithm by means of ...

• Context:
  • It can be implemented by a Covariance-based PCA System.
  • …
• Counter-Example(s):
  • a Correlation-based PCA.
• See: Covariance Matrix, Conjugate Transpose, Transpose, Bessel's Correction, Eigenvector, Diagonalizable Matrix, Diagonal Matrix, Eigenvalue.

---

---

References

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Principal_component_analysis#Computing_PCA_using_the_covariance_method Retrieved:2019-10-14.
  • The following is a detailed description of PCA using the covariance method (see also here) as opposed to the correlation method.

    The goal is to transform a given data set X of dimension p to an alternative data set Y of smaller dimension L. Equivalently, we are seeking to find the matrix Y, where Y is the Karhunen–Loève transform (KLT) of matrix X: : [math]\displaystyle{ \mathbf{Y} = \mathbb{KLT} \{ \mathbf{X} \} }[/math]

---