Reducing Complexity in Vehicle Recognition: A Comparison of Dimensionality Reduction Methods for Acoustic Features

Abstract

Reducing the complexity of high-dimensional acoustic data is essential for effective vehicle recognition, especially in intelligent transportation systems. This study evaluated six dimension reduction techniques, including Principal Component Analysis (PCA), Kernel PCA, Incremental PCA, Independent Component Analysis (ICA), Truncated Singular Value Decomposition (SVD), and Latent Dirichlet Allocation (LDA), to address the challenges of data redundancy while maintaining relevant features. The dataset includes acoustic signals from seven categories of vehicles extracted using Mel-Frequency Cepstral Coefficients (MFCC), Spectral Centroid, and Spectral Bandwidth. Incremental PCA showed the highest accuracy (0.982) on scenarios with larger training datasets, with effective management of high-dimensional data. ICAs provide optimal performance with fewer components at a higher proportion of test data, demonstrating their efficiency in retaining information. SVD shows stability across all data ratios, confirming its reliability for a wide range of applications. Although LDAs maintain competitive results, their interpretability stands out in certain tasks. These findings emphasize the importance of selecting appropriate dimension reduction methods based on data characteristics and application needs, providing valuable insights to improve the accuracy and efficiency of vehicle recognition systems.