Several Pattern Recognition Methods with MATLAB Implementation

This article presents several pattern recognition methods implemented using MATLAB, covering Chebyshev distance, Mahalanobis distance, and Euclidean distance approaches. While multiple programming implementations are provided for each method, we will offer detailed explanations to help readers better understand and apply these techniques. Key implementation aspects include MATLAB's pdist function for distance calculations, covariance matrix handling for Mahalanobis distance, and vectorization techniques for efficient computation. We will discuss each method's advantages and limitations, appropriate application scenarios, and practical parameter adjustment strategies. The article also explores the historical development and recent research advancements in these methods, providing readers with comprehensive insights and deeper understanding of pattern recognition algorithms. Code examples will demonstrate proper data preprocessing, distance metric selection, and classification decision boundaries using MATLAB's statistical and machine learning toolboxes.