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PCA Mathematics Explained with Python by Аурел П is a document available to read on EtoBox.
What is PCA Mathematics Explained with Python about?
The document explains the steps to perform principal component analysis (PCA) on a dataset. It begins with standardizing the dataset, then calculating the covariance matrix using both population and sample formulas. Eigenvalues and eigenvectors are extracted from the covariance matrix. The top eigenvectors corresponding to the highest eigenvalues are kept to transform the standardized data into a lower-dimensional principal component space. The results are verified using NumPy linear algebra functions and b
- Author
- Аурел П
- Language
- EN