pääsuuntavektorit
Pääsuuntavektorit, often translated as principal component vectors or eigenvectors, are a fundamental concept in linear algebra and dimensionality reduction techniques, particularly Principal Component Analysis (PCA). In essence, these vectors represent the directions of maximum variance in a dataset. When analyzing a multi-dimensional dataset, it's common to find that the data points are not spread uniformly in all directions. Instead, there are certain directions along which the data exhibits the most significant variation. These directions are precisely what the principal component vectors capture.
Mathematically, principal component vectors are the eigenvectors of the covariance matrix of the dataset. The covariance
The primary utility of principal component vectors lies in their ability to reduce the dimensionality of data