Matrix Normalization: A Comprehensive Guide For Data Analysis And Modeling
Matrix normalization involves transforming data to enhance its suitability for analysis and modeling. It improves data comparability, reduces the influence of outliers, and makes algorithms more efficient. Common methods include Min-Max, Z-Score, decimal scaling, L1/L2, row/column normalization. Choosing the appropriate method depends on data distribution, desired outcomes, and algorithm requirements. Normalization finds applications in machine…