MATLAB Implementation of Pattern Recognition Assignment Using K-Nearest Neighbors Algorithm

In the field of pattern recognition, the K-Nearest Neighbors (KNN) algorithm serves as a simple yet powerful classification method. This article demonstrates how to implement a basic KNN classifier using MATLAB to complete pattern recognition assignments. The core principle of KNN algorithm follows "birds of a feather flock together": given a test sample, the algorithm calculates its distance to each sample in the training set, identifies the K closest neighbors, and determines the test sample's category through majority voting among these neighbors. ### Implementation Approach Data Preparation Begin by preparing training and test datasets. The training set contains samples with known labels, while the test set consists of samples awaiting classification. Data is typically stored in matrix format where each row represents a sample, with the last column containing class labels. Distance Calculation Common distance metrics include Euclidean distance and Manhattan distance. MATLAB's built-in vectorized operations efficiently handle these computations. The `pdist2` function provides a convenient way to calculate distances between test samples and all training samples, returning a distance matrix where element (i,j) represents the distance between test sample i and training sample j. Finding K-Nearest Neighbors For each test sample, sort distances in ascending order and select the top K samples. MATLAB's `sort` function with appropriate parameters ([sorted_dist, indices] = sort(distances, 2)) quickly accomplishes this task, where the indices help identify the corresponding training samples. Voting for Classification Count the class labels among K nearest neighbors and assign the test sample to the majority class. In case of ties (equal votes), implement random selection or advanced optimization techniques like weighted voting based on distance. ### Optimization and Extensions K-value Selection: Small K values increase sensitivity to noise, while large K values blur classification boundaries. Use cross-validation (e.g., MATLAB's `crossval` function) to determine optimal K. Normalization Processing: Feature scaling differences may distort distance calculations. Apply normalization techniques like `zscore` standardization to ensure equal feature contribution. Efficient Computation: For large datasets, consider implementing KD-tree structures or approximate nearest neighbor algorithms using MATLAB's `knnsearch` function to accelerate neighbor search operations. Despite its simplicity, KNN performs exceptionally well in many practical applications, particularly suitable for small-scale datasets and low-dimensional feature spaces. MATLAB's powerful matrix computation capabilities make the algorithm implementation highly efficient through vectorized operations that avoid slow loop-based calculations.