论文标题
一种基于压缩感应的最小二乘方法，用于半监督的本地群集提取
A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction
论文作者
论文摘要
提出了基于压缩传感的想法的最小二乘半监督的局部聚类算法，以从具有已知邻接矩阵的图中提取簇。该算法基于与\ cite {laimckenzie2020}中的两阶段方法。然而，在较弱的假设和计算复杂性下，比\ cite {laimckenzie2020}中的假设较少，该算法被证明能够找到具有较高概率的所需群集。我们方法的``一个群集的特征''将其与其他全球聚类方法区分开来。在综合数据（如随机块模型）和真实数据（例如MNIST，政治博客网络），at＆＆t和Yaleb人体面孔数据集上进行了几个数字实验，以证明我们的algority angergorithm。
A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix. The algorithm is based on a two-stage approach similar to the one in \cite{LaiMckenzie2020}. However, under a weaker assumption and with less computational complexity than the one in \cite{LaiMckenzie2020}, the algorithm is shown to be able to find a desired cluster with high probability. The ``one cluster at a time" feature of our method distinguishes it from other global clustering methods. Several numerical experiments are conducted on the synthetic data such as stochastic block model and real data such as MNIST, political blogs network, AT\&T and YaleB human faces data sets to demonstrate the effectiveness and efficiency of our algorithm.
