Complete Guide to Principal Component Analysis
Why is PCA Important?
PCA plays a crucial role in various fields, including data analysis, image processing, and machine learning. It allows us to simplify complex datasets by identifying the most significant patterns and relationships. Additionally, PCA helps in reducing noise and redundancy in the data, enabling more efficient analysis and modeling.
How does PCA work?
PCA works by performing a mathematical transformation on the data, known as basis transformation. This transformation aligns the axes of the data with the directions of maximum variance. The transformed data is represented in terms of principal components, which are obtained by calculating the eigenvectors of the covariance matrix of the original dataset.
