# Principal component analysis (PCA)

Principal component analysis (PCA) is a powerful mathematical technique to reduce the complexity of data. It detects linear combinations of the input fields that can best capture the variance in the entire set of fields, where the components are orthogonal to and not correlated with each other.

The goal is to find a small number of derived fields (principal components) that effectively summarize the information in the original set of input fields.