子空间和运动分割的贴近局部子空间算法
Nearness to Local Subspace Algorithm for Subspace and Motion Segmentation
摘要：
在计算机科学、工程学和数学中，人们越来越关注用子空间和流形的并集来建模信号。从子空间的并集中提取的高维数据的子空间分割和聚类对于计算机视觉、图像和信号处理、通信和信息论中的许多实际应用尤其重要。本文提出了一种高维数据的聚类算法，它来自于一个低维子空间的相等和已知维度的并集。这种情况出现在许多数据聚类问题中，如运动分割和人脸识别。该算法在存在噪声的情况下是可靠的，并应用于Hopkins 155数据集，它生成了迄今为止最好的运动分割结果。视频序列的两个运动、三个运动和整体分割率分别为99.43%、98.69%和99.24%。
英文摘要：
There is a growing interest in computer science, engineering, and mathematics for modeling signals in terms of union of subspaces and manifolds. Subspace segmentation and clustering of high dimensional data drawn from a union of subspaces are especially important with many practical applications in computer vision, image and signal processing, communications, and information theory. This paper presents a clustering algorithm for high dimensional data that comes from a union of lower dimensional subspaces of equal and known dimensions. Such cases occur in many data clustering problems, such as motion segmentation and face recognition. The algorithm is reliable in the presence of noise, and applied to the Hopkins 155 Dataset, it generates the best results to date for motion segmentation. The two motion, three motion, and overall segmentation rates for the video sequences are 99.43%, 98.69%, and 99.24%, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
