Principal Component Analysis Algorithm (PCA)

This text discusses a Principal Component Analysis (PCA) algorithm developed by international researchers, which plays a crucial role in the field of data analysis. The PCA algorithm transforms original data into a new set of variables called principal components that better explain the variance in the original dataset. The fundamental mathematical approach involves computing eigenvectors and eigenvalues from the covariance matrix of standardized data. Dimensionality reduction is achieved by selecting the top k principal components that capture the maximum variance, making data easier to visualize and interpret. In practical implementation, key steps typically include data standardization, covariance matrix computation, eigenvalue decomposition, and component selection. Consequently, PCA has become widely adopted for data exploration and visualization, establishing itself as an indispensable tool in data science. To learn more about PCA algorithms, one can refer to relevant literature and technical articles covering its mathematical foundation and programming implementation.