2017/04/27 by Boyue Wang, Yongli Hu, Wang, Boyue +10 · 1 citation
Computer Science · Engineering · Mathematics · #Artificial intelligence #Cluster analysis #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Curse of dimensionality #Dimension (graph theory) #Dimensionality reduction #Discriminative model #FOS: Computer and information sciences #Face and Expression Recognition #Grassmannian #Image Retrieval and Classification Techniques #Locality #Manifold (fluid mechanics) #Manifold alignment #Mathematics #Nonlinear dimensionality reduction #Pattern recognition (psychology) #Pure mathematics #Remote-Sensing Image Classification #cs.CV
paper · pdf · doi:10.48550/arxiv.1704.08458
published in arXiv (Cornell University) (Cornell University) · Accepted by IJCAI 2017
arxiv created 2017/04/27 · openalex publication_date 2017/04/27 · arxiv updated 2017/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show its clear advantages over other Grassmann based algorithms.