The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

Currently, supervised person re-identification (Re-ID) models trained on labeled datasets can achieve high recognition performance in the same data domain. However, accuracy drops dramatically when these models are directly applied to other unlabeled datasets or natural environments, due to a significant sample distribution gap between the two domains. Unsupervised Domain Adaptation (UDA) methods can solve this problem by fine-tuning the model on the target dataset with pseudo-labels generated by the clustering method. Yet, these methods are primarily aimed at the image-based person Re-ID domain. This is because the background noise and interference information are complex and changeable in the video scenarios, resulting in large intra-class distances and small inter-class spaces, which easily lead to noisy labels. Huge domain gap and noisy labels hinder clustering and training processes heavily in the video-based person Re-ID. To address the problem, we propose a novel UDA method via Dynamic Clustering and Co-segment Attentive Learning (DCCAL) for it. DCCAL includes a Dynamic Clustering (DC) module and a Co-segment Attentive Learning (CAL) module to alleviate noisy labels by clustering pedestrians adaptively within different generation processes and reducing domain gap with a co-segmentation-based attention mechanism, respectively. Additionally, we introduce Kullback-Leibler (KL) divergence loss to reduce the distribution of features between two domains for better performance. Experimental results on two large-scale video-based person Re-ID datasets, MARS and DukeMTMC-VideoReID (DukeV), demonstrate exceptional precision performance. Our method outperforms state-of-the-art semi-supervised and unsupervised approaches by 1.1% in Rank-1 and 1.5% in mAP on DukeV, as well as 3.1% and 2.1% in Rank-1 and mAP on MARS, respectively.