MATLAB Implementation of Normalization Algorithms with Code Examples

In this article, I will provide a comprehensive overview of normalization algorithms. Normalization serves as a crucial tool for comparing and analyzing data across different scales. To establish fundamental understanding, I will first explain data scaling challenges and the rationale behind normalization. Subsequently, I will detail commonly used normalization techniques including Min-Max Scaling, Z-score Standardization, and Decimal Scaling normalization. Each method will be accompanied by MATLAB implementation insights: Min-Max Scaling linearly transforms data to [0,1] range using (x-min)/(max-min) computation; Z-score Standardization centers data around mean with unit variance through (x-μ)/σ calculation; Decimal Scaling normalizes by moving decimal points based on maximum absolute values. Finally, I will share well-structured normalization source code that I extensively curated, featuring modular functions with automated parameter detection and boundary handling. The implementation includes error checking for invalid inputs and supports both vector and matrix operations through MATLAB's vectorization capabilities.