Principal Component Analysis (PCA)

About Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a statistical procedure used to convert a set of observations of possibly correlated variables into a set of linearly uncorrelated variables called principal components.

The output is a set of transformed variables called principal components, which are ranked based on the variance in the data that they capture. PCA is widely used for dimensionality reduction in machine learning and data visualization.

It cannot be expressed as a single JavaScript function due to its complexity and iterative nature.

Examples of Use

PCA is often utilized in exploratory data analysis, feature extraction, pattern recognition, and data compression.

Limitations

Even though PCA is a powerful technique, it has some limitations such as the assumption of linearity, sensitivity to scale, and lack of interpretability of the principal components.