Support Vector Machine (SVM) for Enhanced k-Nearest Neighbor Classification with Dimensionality Reduction

In this paper, we present a detailed analysis of how Support Vector Machine (SVM) can be utilized as a powerful tool to enhance the k-nearest neighbor (kNN) classifier. Our approach introduces a novel multi-class dimensionality reduction technique called Discriminant Analysis via Support Vectors (SVDA), which leverages SVM's capability to identify critical data points. The algorithm implementation involves computing transformation matrices exclusively from support vectors, significantly reducing computational complexity for kernel-based feature extraction. This technique naturally extends to non-linear versions through kernel mapping, resulting in Kernel Discriminant via Support Vectors (SVKD). Key implementation aspects include using SVM's structural risk minimization principle to select the most informative data points, thereby optimizing the dimensionality reduction process. Our experiments conducted on several standard databases demonstrate substantial improvement in LDA-based recognition accuracy. The proposed SVDA approach therefore offers an efficient solution for enhancing both computational performance and classification accuracy in kNN applications. Furthermore, we discuss potential applications of SVM across various domains including image recognition, natural language processing, and speech recognition. We emphasize SVM's key advantages: effective handling of high-dimensional data through kernel tricks, robustness against noise and outliers via margin maximization, and flexibility in managing non-linearly separable data through different kernel functions. The implementation typically involves solving convex optimization problems using libraries like LIBSVM or scikit-learn, ensuring global optimum solutions. Our findings confirm SVM's versatility as an effective tool for diverse classification challenges across multiple domains.