MATLAB Implementation of Minimum Distance Classifier

The minimum distance classifier is a supervised machine learning algorithm that categorizes data points by measuring their proximity to predefined class centroids. In MATLAB implementation, this typically involves calculating Euclidean distances between input vectors and class prototypes using built-in functions like pdist2() or norm() for efficient distance computation. The core algorithm operates by first establishing class centers through training data, then for each new data point, computing the Euclidean distance to all class centers using vectorized operations. The classification decision is made by selecting the class with the minimum distance, which can be efficiently implemented using MATLAB's min() function with appropriate indexing. This classifier demonstrates particular effectiveness in scenarios with well-separated classes and can be optimized through feature scaling using zscore() or similar normalization functions. Key advantages include computational simplicity, fast execution speed using MATLAB's matrix operations, and easy integration with other toolboxes like Statistics and Machine Learning Toolbox. Common applications span image recognition (using pixel feature vectors), speech recognition (MFCC feature classification), and natural language processing (word embedding clustering), where the implementation often involves reshaping multidimensional data into appropriate feature vectors before distance calculation.