Pääjakautuminenmenetelmä

Pääjakautuminenmenetelmä, also known as the principal component analysis (PCA), is a statistical method used for dimensionality reduction. It transforms a set of possibly correlated variables into a set of linearly uncorrelated variables called principal components. The goal is to capture the maximum possible variance in the original data with a reduced number of components.

The first principal component is the linear combination of the original variables that explains the largest proportion of the total variance. Subsequent principal components are constructed in a similar manner, each orthogonal to the preceding ones and capturing the next largest amount of remaining variance. This process continues until a desired level of variance is explained or a specific number of components is reached.

PCA is widely applied in various fields, including data visualization, noise reduction, and feature extraction for machine learning algorithms. By reducing the number of variables, PCA can simplify complex datasets, making them easier to analyze and interpret, and potentially improving the performance of predictive models. The interpretation of principal components can sometimes be challenging as they are linear combinations of the original features.