论文标题
稀疏子空间聚类的分析：实验和随机投影
Analysis of Sparse Subspace Clustering: Experiments and Random Projection
论文作者
论文摘要
聚类可以定义为将对象组装成许多元素以某种方式相似的组的过程。作为一种用于许多领域的技术，例如面部聚类，植物分类，图像分割，文档分类，聚类被认为是最重要的无监督学习问题之一。科学家多年来一直调查了这个问题，并开发了可以解决该问题的不同技术，例如K-均值聚类。我们分析了其中一种技术：一种称为稀疏子空间聚类的强大聚类算法。我们使用此方法演示了几个实验，然后引入了一种新方法，该方法可以减少执行稀疏子空间群集所需的计算时间。
Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner. As a technique that is used in many domains, such as face clustering, plant categorization, image segmentation, document classification, clustering is considered one of the most important unsupervised learning problems. Scientists have surveyed this problem for years and developed different techniques that can solve it, such as k-means clustering. We analyze one of these techniques: a powerful clustering algorithm called Sparse Subspace Clustering. We demonstrate several experiments using this method and then introduce a new approach that can reduce the computational time required to perform sparse subspace clustering.
