2015/08/06 by Bibo Shi, Jundong Liu, Shi, Bibo +1
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Human Pose and Action Recognition #Machine Learning (cs.LG) #Video Surveillance and Tracking Methods #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1508.01534
arxiv created 2015/08/06 · openalex publication_date 2015/08/06 · arxiv updated 2015/08/10 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
In recent years, research efforts to extend linear metric learning models to handle nonlinear structures have attracted great interests. In this paper, we propose a novel nonlinear solution through the utilization of deformable geometric models to learn spatially varying metrics, and apply the strategy to boost the performance of both kNN and SVM classifiers. Thin-plate splines (TPS) are chosen as the geometric model due to their remarkable versatility and representation power in accounting for high-order deformations. By transforming the input space through TPS, we can pull same-class neighbors closer while pushing different-class points farther away in kNN, as well as make the input data points more linearly separable in SVMs. Improvements in the performance of kNN classification are demonstrated through experiments on synthetic and real world datasets, with comparisons made with several state-of-the-art metric learning solutions. Our SVM-based models also achieve significant improvements over traditional linear and kernel SVMs with the same datasets.