Non-Convex Metric Learning-Based Trajectory Clustering Algorithm

To address the issue of suboptimal clustering performance arising from the limitations of distance measurement in traditional trajectory clustering methods, this paper presents a novel trajectory clustering strategy that integrates the bag-of-words model with non-convex metric learning. Initially, the strategy extracts motion characteristic parameters from trajectory points. Subsequently, based on the minimum description length criterion, trajectories are segmented into several homogeneous segments, and statistical properties for each segment are computed. A non-convex metric learning mechanism is then introduced to enhance similarity evaluation accuracy. Furthermore, by combining a bag-of-words model with a non-convex metric learning algorithm, segmented trajectory fragments are transformed into fixed-length feature descriptors. Finally, the K-means method and the proposed non-convex metric learning algorithm are utilized to analyze the feature descriptors, and hence, the effective clustering of trajectories can be achieved. Experimental results demonstrate that the proposed method exhibits superior clustering performance compared to the state-of-the-art trajectory clustering approaches.